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Course Overview
Schedule of Classes
Course Curriculum
48 Subjects
SKT_PG_Theory_Core Python
96 Learning Materials
Introduction to programming languages
Introduction to Programming Language
External Link
Types of Computer Languages
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Evolution of Computer Languages - 1
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Evolution of Computer Languages - 2
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Programming Paradigms - 1
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Programming Paradigms - 2
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Programming Paradigms - 3
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Programming Paradigms - 4
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Programming Translators
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Types of Scripts
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Logics Building
Logics Building & Flowchart
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Algorithm & Pseudocode
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Introduction to Python
Introduction to Python
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Python Limitations and Libraries
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History of Python
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Features of Python
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Python Applications
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Python Implementations
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Python vs other Languages
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Characteristics of Python
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Python Environment Setup
Downloading & Installation of Python
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Python Real-time IDEs
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Python Program Execution
Python Program Execution
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Basic Syntax
Comments and Indentations in Python
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Keywords and Identifiers
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Variables
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Input and Output Operations
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Data Types
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Type Conversions
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Operators
Introduction to Operators
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Arithmetic Operators
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Assignment Operators
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Relational Operators
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Logical Operators
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Bitwise Operators
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Membership Operators
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Identity Operators
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Conditional Statements
Introduction to Conditional Statement
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Simple If Statement
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If-Else Statement
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If-Elif-Else Statement
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Nested If-Else Statement
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Short-Hand If Statement
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Jump Statement
Match Case
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Range() Function & Del Keyword
Range() Function and Delete Keyword
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Loops
Introduction to Loops
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While Loop
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For Loop
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Nested Loops
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Loop Control Statements
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Functions
Introduction to Functions
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Implementing Functions
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Classification of Functions
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Nested Functions
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Scope of Variables in Functions
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First Class Functions
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Anonymous Functions
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Recursive Functions
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Lists
Introduction to Lists
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Accessing List Elements
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List Operations
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Built in List Functions
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List Methods-1
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List Methods-2
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List Methods-3
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Tuples
Introduction to Tuples
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Accessing Tuple Elements
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Iterating Tuple Elements
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Built-in Tuple Functions
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Tuple Methods
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Strings - 1
Introduction to Strings
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Accessing Values from Strings
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String Operations - 1
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String Operations - 2
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String Manipulation Methods
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Strings - 2
String Validation and Transformation Methods
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String Analysis Methods
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Numeric String Methods
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String Searching and Manipulation
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Strings - 3
Advanced String Operations
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String Alignment Methods
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String Formatting Methods
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String Splitting Methods
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Mastering in String Operations
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Dictionaries
Introduction to Dictionaries
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Working With Dictionaries
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Accessing Keys & Values from Dictinaries
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Built-in Dictionary Methods-1
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Built-in Dictionary Methods-2
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Sets
Introduction to Sets
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Working with Sets
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Sets Built-in Methods - 1
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Sets Built-in Methods - 2
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Set Operations-1
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Set Operations-2
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Comprehensions
Comprehensions
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SKT_PG_Theory_Advance Python
35 Learning Materials
Memory Management
Dynamic Typing
External Link
Modules & Packages
Modules
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Importing Modules
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Creating Modules
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Modules Search Path
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What are Packages
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Creating Packages
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Python DateTime
Data Time Module
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Time module
Time Module - 1
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Time Module - 2
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Python Calendar Module
Calendar Module
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Random Module
Random Module
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Cryptographically Secure Random Generator
UUID Module
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Other Modules in Python
OS Module
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Sys Module
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Logger Module
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JSON Module
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Exception Handling
Introduction to Exception Handling
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Types of Exception Handling
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Errors in Exception Handling
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Working with Exception Handling
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Raising Exception and Creating User Defined Exception
Raising an Exception
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Exception Chaining
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Exception Lifecycle
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Warnings in Exception Handling
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File Handling in Python
Introduction to File Handling
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Types of Files & File Paths
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Types of File Access Modes
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Types of Binary File Access Modes
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Working with Binary File Access Modes
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Create File in Python
Creating an Empty Text File
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Creating File In A Specific Directory
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Open a File in Python
Access Modes for Opening a File
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Read File in Python
Reading a File
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Reading a File into List
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SKT_PG_Theory_OOPS with Python
27 Learning Materials
Introduction to OOPs
Introduction to Object Oriented Programming
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Classes and Objectes
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Class Variables and Instance Variables
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Constructor & Destructor Methods
Constructor method
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Destructor Method
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Types of methods
Types of methods
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Inheritance
Introduction to inheritance
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Multiple Inheritance
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Multilevel Inheritance
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Hierarchial Inheritance
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Hybrid Inheritance
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More on Inheritance
Super() function
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Method Overriding
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Method Resolution Order
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Polymorphism
Introduction to Polymorphism
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Polymorphic Function and Duck Typing
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Method Overriding
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Method Overloading
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Operator Overloading
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Introduction to encapsulation
Introduction to Encapsulation
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Access Modifiers
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Getters and Setters
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Abstraction
Abstraction
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Closures and Decorators
Closures
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Decorators
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Iterators and Generators
Iterators
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Generators
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OLD_SKT_Python Engg Applications
14 Learning Materials
Transpose of a Matrix without using NumPy
Introduction to Transpose of Matrix
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Function to Input a Matrix : Code Explanation
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Function to Input a Matrix : Code Implementation
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Function to Transpose a Matrix : Code Explanation
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Function to Transpose a Matrix : Code Implementation
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Comparing with In-Built Function in NumPy
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Determinant of Matrix without using NumPy
Manually Calculating Determinant of Matrix
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Laplace Expansion to Calculate Determinant
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Function to Find Determinant : Code Explanation
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Function to Find Determinant : Code Implementation
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Comparing with In-Built Function in NumPy
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Cofactor & Minor Matrix without using NumPy
Introduction to Cofactors & Minor Matrix
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Function for Cofactors & Minor Matrix : Code Explanation
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Function for Cofactors & Minor Matrix : Code Implementation
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SKT_DS_Theory_Data Science
42 Learning Materials
About Course
Course Overview
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The Data Revolution: How Data is Changing Our World
Introduction to Data Revolution
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Data Ascendancy: Unleashing the Power Within
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Historical Overview of Data Usage
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Current Trends in Data Usage
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Introduction to Data Science
Introduction to Data Science
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Unpacking the Confusion: How Data Science Stands Apart from Other Related Fields
Unpacking the Confusion:Data Analysis
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Unpacking the Confusion: Data Analytics
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Unpacking the Confusion: Data Mining & Data Engineering
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Unpacking the Confusion: Business Analytics & Predictive Anlaytics
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Unpacking the Confusion: AI, ML and DL
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Unpacking the Confusion: Big Data
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Exploring In-Demand Careers
Introduction to In-Demand Careers
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Identifying High-Demand Careers
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Data Science and Analytics Careers
Data Science and Analytics Careers
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Data Scientist
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Data Analyst
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Machine Learning Engineer
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Business Intelligence Analyst
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Data Engineer
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Data Science Life Cycle
Introduction to Data Science Life Cycle
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Phases of the Data Science Life Cycle
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Data Science Life Cycle- Problem Identification & Project Planning
Problem Identification and Project Planning-1
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Problem Identification and Project Planning-2
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Data Science Life Cycle - Data Acquisition and Understanding
Data Acquisition and Understanding
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Data Science Life Cycle - Data Preparation and Cleaning
Introduction to Data Preparation and Cleaning
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Missing Values and Outliers
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Raw Data Transformation for Numerical & Categorical
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Data Science Life Cycle - Exploratory Data Analysis (EDA)
Introduction to Exploratory Data Analysis (EDA)
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Exploring Data
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Data Science Life Cycle -Feature Engineering and Selection
Feature Engineering & Feature Selection
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Data Science Life Cycle -Model Building and Evaluation
Building Machine Learning Models
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Evaluating Classification Models
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Evaluating Regression Models
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Real World Use Cases
Churn Prediction
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Fraud Detection
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Customer Segmentation
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Recommendation Engines
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Sentiment Analysis
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Image and Object Recognition
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Speech and Language Recognition
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Healthcare Analytics
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SKT_DS_Theory_Anaconda Essentials
5 Learning Materials
Essential Programming Languages used for ML
Essential Programming Languages used for ML
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Anaconda
Introduction to Anaconda
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Installing Anaconda
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Anaconda Environment
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Jupyter Notebook
Jupyter Notebook
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SKT_DS_Theory_Pandas
50 Learning Materials
Introduction to Pandas
Introduction to Pandas
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Working with Pandas
Working with Pandas
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Pandas Series-1
Introduction to Series
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Creating Series from Lists
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Creating Series from Dictionary
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Creating Series from NumPy Array
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Creating a Series from Scalar Value
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Creating Series from NaN Values & Index Argument
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Pandas Series-2
Working with Series Indexing
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Accessing Data from Series
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Accessing Data from Series using Slicing
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Accessing Data from Series using loc & iloc
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Series Attributes-1
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Series Attributes-2
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Pandas Series-3
Sorting and Converting Type of Elements in Series
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Working with Null Values in Series
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Summarising Series-1
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Summarising Series-2
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Working with Duplicate Values
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Pandas Series - 4
Arithmetic Operations on Pandas Series
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Comparison / Relational Operations with Pandas Series - 1
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Comparison / Relational Operations with Pandas Series - 2
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Comparison / Relational Operations with Pandas Series - 3
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Logical Operators on Pandas Series
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Manipulating Pandas Series
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Pandas DataFrame - 1
Introduction to Pandas DataFrame
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Creating Pandas DataFrame
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DataFrame Attributes - 1
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DataFrame Attributes - 2
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Converting and Sorting Elements
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Working with Null Values in DataFrame
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Pandas DataFrame - 2
Summarising DataFrame - 1
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Summarising DataFrame - 2
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Working with Duplicates
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Arithmetic Operations on Pandas Dataframe - 1
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Arithmetic Operations on Pandas Dataframe - 2
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Pandas DataFrame - 3
Comparison Operations on Pandas Dataframe - 1
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Comparison Operations on Pandas Dataframe - 2
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Comparison Operations on Pandas Dataframe - 3
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Logical Operations on Pandas Dataframe - 1
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Logical Operations on Pandas Dataframe - 2
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Manipulating pandas Dataframe
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Pandas DataFrame - 4
Accessing Single Column in a DataFrame
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Accessing Multiple Columns
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Accessing Rows in a DataFrame
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Accessing Single Element in a DataFrame
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Reading Data from variuos types of files
Reading & Saving CSV File
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Reading & Saving Excel File
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Reading & Saving JSON File
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Reading Data from SQL Database
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SKT_DS_Lab L1_Pandas
2 Learning Materials
Basic Level Programming on Pandas Series-1
Write a code to create a Series from a NumPy array of random numbers and Print the Series.
External Link
Create a Series with three NaN values, and specify index labels as 'A', 'B', and 'C'.
External Link
Basic Level Programming on Pandas Series-2
Basic Level Programming on Pandas Series-3
Basic Level Programming on Pandas Series - 4
Basic Level Programming on Pandas DataFrame - 1
SKT_DS_Lab L2_Pandas
2 Learning Materials
Intermediate Level Programming on Pandas Series-1
Create a Series and set custom index labels in Pandas
External Link
Create a Series with both positive and negative values and print its mean.
External Link
Intermediate Level Programming on Pandas Series-2
Intermediate Level Programming on Pandas Series-3
Intermediate Level Programming on Pandas Series - 4
Intermediate Level Programming on Pandas DataFrame - 1
SKT_DS_Lab L3_Pandas
2 Learning Materials
Advance Level Programming on Pandas Series-1
Construct a Pandas Series from dictionary {'USA': 331, 'India': 1380, 'China': 1441, 'Brazil': 213}
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Python program using pandas library representing the marks of five students
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Advance Level Programming on Pandas Series-2
Advance Level Programming on Pandas Series-3
Advance Level Programming on Pandas Series - 4
Advance Level Programming on Pandas DataFrame - 1
SKT_DS_Theory_R Programming
64 Learning Materials
About Course
About Course
External Link
Introduction to R Programming
Introduction to R Programming
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Setting up R Environment
Installing CRAN for R
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Installing RStudio for R Programming
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Walking through RStudio
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Using R as Calculator
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Basic Syntax
Comments in R
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Keywords & Identifiers
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Variables & Constants
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Input & Output Operations
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Data Types
Data Types
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Data Structures in R
Introduction to Data Structures in R
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Strings
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Vectors
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Lists
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Arrays
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Matrices
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DataFrames
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Factors
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Operators
Introduction to Operators
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Arithmetic Operators
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Assignment Operators
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Comparison Operators
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Logical Operators
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Bitwise Operators
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Bitwise Shift Operators
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Membership Operators
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Conditional Statements
Conditional Statements
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If Statement
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If - Else Statement
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Else - If Ladder Statement
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Nested If-Else Statement
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If Else Function
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Loops
Loops-1
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Loops-2
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Branching Statements
Branching Statements
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Functions
Introduction to Functions
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Advantages of Functions
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Implementation of Functions
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Function Arguments
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Return Values
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Recursive Functions
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Nested Functions
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Scope of Variables
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Vectors
Introduction to Vectors
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Creating a Vector
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Types of Vectors
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Accessing Elements in Vector
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Vector Operations & Manipulations - 1
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Vector Operations & Manipulations - 2
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Vector Operations & Manipulations - 3
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Strings
Rules for Declaring Strings
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Accessing String Elements
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String Manipulations - 1
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String Manipulations - 2
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String Manipulations - 3
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String Manipulation Methods - 1
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String Manipulation Methods - 2
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Escape Sequence in String
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Lists
R Predefined Lists
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Accessing List Elements
External Link
Iterating & Manipulating List elements
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Working with List - 1
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Working with List - 2
External Link
SKT_DS_Lab L1_R Programming
2 Learning Materials
Basic Level Programming on Basic Syntax
Write a R program that prints the values of variables with valid identifiers
External Link
Write a program using `pi` as a constant and print
External Link
Basic Level Programming on Data Types
Basic Level Programming on Data Structures in R
Basic Level Programming on Operators
Basic Level Programming on Conditional Statements
SKT_DS_Lab L2_R Programming
2 Learning Materials
Intermediate Level Programming on Basic Syntax
Write a R program with comments that explain the steps for calculating the area of a circle.
External Link
Write a program that uses reserved keywords like (for, while, if, else) inside a comment.
External Link
Intermediate Level Programming on Data Types
Intermediate Level Programming on Data Structures in R
Intermediate Level Programming on Operators
Intermediate Level Programming on Conditional Statements
SKT_DS_Lab L3_R Programming
2 Learning Materials
Advance Level Programming on Basic Syntax
Write a R program demonstrating valid and invalid identifiers with comments explaining the errors.
External Link
Create a R program where a constant value is defined for a sales tax rate and used in a price cal
External Link
Advance Level Programming on Data Types
Advance Level Programming on Data Structures in R
Advance Level Programming on Operators
Advance Level Programming on Conditional Statements
SKT_DS_Theory_Data Analasys & Vis
143 Learning Materials
About Course
Course Overview
External Link
Introduction to Data Analysis
What is Data Analysis?
External Link
Why is Data Analysis important?
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Sources of Data
External Link
Sampling Techniques
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Data Quality Issues
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Data Management & Storage
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Introduction to Data Visualization
Introduction to Data Visualization
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Data Wrangling
Introduction to Data Wrangling
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Subsetting a Dataset
Subsetting a Dataset
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Selecting Columns
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Selecting Rows
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Selecting Rows & Columns
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Slicing Columns
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Slicing Rows
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Slicing Rows & Columns
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Indexing
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Filtering
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Combining and Merging Data Sets
Database-Style Merging Datasets
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Merging on Index - Outer
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Merging on Index - Left
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Merging on Index - Right
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Concatenating Along an Axis
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Combining Data with Overlap
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Reshaping
Reshaping
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Data Transformation
Introduction for Data Transformation
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Removing Duplicates & Replacing Values
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Transforming Data Using a Function or Mapping
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Renaming Axis Indexes
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Data Discretization
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String Manipulation
String Manipulation
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Group By Operations
Introduction to Group By
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Exploring get_group
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Group by Syntax & Iterating Over Groups
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Grouping with Dicts and Series
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Grouping with Functions
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Grouping By Index Levels
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Data Aggregation
Introduction to Data Aggregation
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Syntax of Data Aggregation
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Column-wise and Multiple Function
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Returning Aggregated Data in "Unindexed" Form
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Group-wise Operations and Transformations
Apply: General split-apply-combine
External Link
Quantile & Bucket Analysis
External Link
Filling Missing Values with Group-specific Values
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Random Sampling & Permutation
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Random Sampling & Permutation using Group By
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Group Weighted Average and Correlation
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Pivot Tables and Cross-Tabulation
Pivot Table
External Link
Cross Tabulation
External Link
Data Preparation & Basic Models
Data Preparation
External Link
Advanced Data Transformation Techniques
Linear Transformation
External Link
Quadratic Transformation
External Link
Non-polynomial Transformation
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Polynomial Transformation
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Rank Transformation
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Box-Cox Transformation
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Histogram
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Missing Values
Introduction to Data Cleaning
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Introduction to Missing Values
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Removing Missing Values
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Mean Imputation
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Median Imputation
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Mode Imputation
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Forward Fill Imputation
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Backward Fill Imputation
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Dealing with Noisy Data
Noisy Data
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Types of Noise Data
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Introduction to Noise Filtering
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Noise Filtering at Data Level
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Enhancing Data Analysis Strategies Against Noise
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Introduction to Outliers
Introduction to Outliers
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Detecting & Handling Outliers
Detecting outliers using Boxplot and IQR
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Detecting outliers using the Z-scores
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Detecting outliers using the percentile method
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Detecting outliers using Standard Deviation method
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Handling Categorical Data
Introduction to Categorical Data
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Categorical Encoding
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One Hot Encoding
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Label Encoding
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Ordinal Encoding
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Helmert Encoding
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Binary Encoding
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Frequency Encoding
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Mean Encoding
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Weight of Evidence Encoding
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Probability Ratio Encoding
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Hashing Encoding
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Backward Difference Encoding
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Leave One Out Encoding
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James Stein Encoding
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M-estimator Encoding
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Thermometer Encoder
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Which Encoding is best?
External Link
Handling Numerical Data
Introduction to Numerical Data
External Link
Introduction to Feature Scaling
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Min- Max Scaling
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Standardization
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Robust Scaling
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Max Abs Scaler
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Exploratory Data Analysis
Introduction to Exploratory Data Analysis
External Link
Intoduction to Univariate Analysis
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Bar Plot for Univariate
External Link
Pie Chart for Univariate
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Count Plot for Univariate
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Histogram for Univariate
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Box PLot for Univariate
External Link
Density Plot for Univariate
External Link
Line Plot for Univariate
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Intoduction to Bivariate Analysis
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Scatter Plot
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Line Plot
External Link
Joint Plot
External Link
Heatmap
External Link
Stacked Bar Plot
External Link
Grouped Bar Plot
External Link
Clustered Heatmap
External Link
Chi-Square Test
External Link
Bar Plot
External Link
Box Plot
External Link
Violin Plot
External Link
Point Plot
External Link
Swarm Plot
External Link
Introduction to Multivariate Analysis
External Link
Heat Map for Multivariate
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Pair plot
External Link
Feature Engineering
Introduction to Feature Engineering
External Link
Feature Selection Techniques & Criteria
External Link
Filter Feature Selection
External Link
Wrapper Feature Selection
External Link
Embedded Feature Selection
External Link
Implications of Feature Selection Methods
External Link
Representative Feature Selection Methods
External Link
Leading and Recent Feature Selection Techniques
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Feature Extraction
External Link
Feature Construction
External Link
Experimental Comparative Analyses in Feature Selection
External Link
Handling Imbalance Data
Introduction to Handle Imbalance Data
External Link
Choose Proper Evaluation Metric
External Link
Resampling
External Link
SMOTE
External Link
Balanced Bagging Classifier
External Link
Threshold moving
External Link
Augmentation
External Link
SKT_DS_Lab L1_Data Analasys & Vis
4 Learning Materials
Basic Level Programming on Subsetting a Dataset
Generate the sample DataFrame and then choose any one of the Columns. Display its contents
External Link
Creating and displaying Data. choose any one of the Row. Display its contents
External Link
Basic Level Programming on Group By Operations
Group the DataFrame by a column and apply multiple aggregation functions
External Link
Group the numbers in the num column they are even or odd and calculate the sum of each group data
External Link
Basic Level Programming on Data Aggregation
Basic Level Programming on Group-wise Operations and Transformations
Basic Level Programming on Pivot Tables and Cross-Tabulation
SKT_DS_Lab L2_Data Analasys & Vis
2 Learning Materials
Intermediate Level Programming on Subsetting a Dataset
Construct a Sample DataFrame use the Slicing feature all columns whose names start with an ‘S’
External Link
Intermediate Level Programming on Group By Operations
Given a list of dictionaries representing customers average order total for each city
External Link
Intermediate Level Programming on Data Aggregation
Intermediate Level Programming on Group-wise Operations and Transformations
Intermediate Level Programming on Pivot Tables and Cross-Tabulation
SKT_DS_Lab L3_Data Analasys & Vis
3 Learning Materials
Advance Level Programming on Subsetting a Dataset
Write a Python Program to import any CSV file to Pandas DataFrame
External Link
Advance Level Programming on Group By Operations
Load, explore a dataset , perform groupby() operations using pandas library and visualise data
External Link
list of dictionaries representing employees group using Sorting higher salaries and longer.
External Link
Advance Level Programming on Data Aggregation
Advance Level Programming on Group-wise Operations and Transformations
Advance Level Programming on Pivot Tables and Cross-Tabulation
SKT_DS_Theory_Mataplotlib
34 Learning Materials
Introduction to Data Visualisation
Introduction to Data Visualisation
External Link
Matplotlib Architecture
Matplotlib Architecture
External Link
Working with Matplotlib
Working with Matplotlib
External Link
Decorators and Styles-1
Grids
External Link
Axes
External Link
Labels
External Link
Markers
External Link
Colors
External Link
Title
External Link
Decorators and Styles-2
Legends
External Link
Ticks
External Link
Font Styles
External Link
Limits
External Link
Decorators and Styles on the Bar Graph
External Link
Different Types of Plots-1
Single Line Plot
External Link
Multi-Line Plot
External Link
Scatter Plot
External Link
Stack Plot
External Link
Stream Plot
External Link
Different Types of Plots-2
Sub-Plot
External Link
Step Plot
External Link
Filled Plot
External Link
Hexagonal Bin Plot
External Link
Different Types of Plots-3
Box Plot
External Link
Violin Plot
External Link
Different Types of Graphs
Bar Graph
External Link
Multi Bar Graph
External Link
Stacked Bar Graph
External Link
Different Types of Charts
Different Types of Charts
External Link
Pie Chart
External Link
Bubble Chart
External Link
Polar Chart
External Link
Histograms and Contours
Histogram
External Link
Contour Plot
External Link
SKT_DS_Theory_Seaborn
34 Learning Materials
Getting Started
Introduction to Seaborn
External Link
Prerequisites & Dependencies
External Link
Installation of Seaborn
External Link
Basics of Seaborn Plotting
Introduction to Plots
External Link
Decorators and Styles - 1
External Link
Decorators and Styles - 2
External Link
Seaborn Figure Styles - 1
External Link
Seaborn Figure Styles - 2
External Link
Seaborn – Color Palettes
External Link
Seaborn Grids
External Link
Relational Plots
Line Plot()
External Link
Scatter Plot()
External Link
Rel Plot()
External Link
Distributions Plots
Histplot()
External Link
KDEplot()
External Link
ECDFplot()
External Link
Rugplot()
External Link
Displot()
External Link
Pairplot()
External Link
Categorical Scatter Plots
Stripplot()
External Link
Swarmplot()
External Link
Categorical Distribution Plots
Boxplot()
External Link
Violinplot()
External Link
Boxenplot()
External Link
Categorical Estimate Plots
Pointplot()
External Link
Barplot()
External Link
Countplot()
External Link
Catplot()
External Link
Statistical Estimations
Error bars
External Link
Estimating Regression Fits
External Link
Multi-Plot Grids
External Link
Advanced Seaborn
Plotting Univariate Distributions
External Link
Plotting Bivariate Distributions
External Link
Seaborn Python Case Study
Data Visualization Using Seaborn on Census Dataset
External Link
SKT_DS_Lab L1_Seaborn
3 Learning Materials
Basic Level Programming on Basics of Seaborn Plotting
Write a program to add titles and labels to a Seaborn plot?
External Link
Write a program to modify line styles and colors in Seaborn plot?
External Link
Write a program to customize legends and markers in Seaborn plots?
External Link
Basic Level Programming on Relational Plots
Basic Level Programming on Distributions Plots
Basic Level Programming on Categorical Scatter Plots
Basic Level Programming on Categorical Distribution Plots
SKT_DS_Lab L2_Seaborn
3 Learning Materials
Intermediate Level Programming on Basics of Seaborn Plotting
Write a program to use multiple line styles in one Seaborn plot?
External Link
Write a program to apply dark theme to a seaborn plot?
External Link
Intermediate Level Programming on Relational Plots
Write a program to create a grouped scatter plot using Seaborn
External Link
Intermediate Level Programming on Distributions Plots
Intermediate Level Programming on Categorical Scatter Plots
Intermediate Level Programming on Categorical Distribution Plots
SKT_DS_Lab L3_Seaborn
3 Learning Materials
Advance Level Programming on Basics of Seaborn Plotting
Write a program to customize and save figures in high resolution in Seaborn?
External Link
Write a program to customize a Seaborn plot with grids, axis labels and markers?
External Link
Advance Level Programming on Relational Plots
Write a program to create a heatmap overlay for a relational plot in Seaborn?
External Link
Advance Level Programming on Distributions Plots
Advance Level Programming on Categorical Scatter Plots
Advance Level Programming on Categorical Distribution Plots
SKT_DS_Theory_Plotly
14 Learning Materials
Introduction to plotly
Introduction to Plotly
External Link
History and Evolution of Plotly
External Link
Basic plot using plotly
Basic charts
External Link
Chart Customization & Interactivity
Styling plotly
External Link
Customize a plot using plotly
External Link
Basic 2D Charts
Line Charts
External Link
Scatter Plots
External Link
Basic Bar Charts
External Link
Grouped Bar Charts
External Link
Stacked Bar Charts
External Link
Pie chart
External Link
Area Charts
External Link
Box Plot
External Link
Notched Boxplot
External Link
SKT_DS_Theory_Statistics and Probability
59 Learning Materials
All About Data
Introduction to Data
External Link
Case Study
External Link
Properties of Data
External Link
Data based on Structure & Application
External Link
Types of Variables
External Link
Relationship between Variables
External Link
Introduction To Statistics
Introduction to Statistics
External Link
History of Statistics
External Link
Fundamental Elements of Statistics
External Link
Statistical Thinking and Methods
External Link
Describing Data with Graphs
Introduction to Describing Data with Graphs
External Link
Graphs for Categorical Data
External Link
Graphs for Numerical Data
External Link
Scatter Plots
External Link
Time Series Plots
External Link
Seasonal Plots
External Link
Trend
External Link
Cyclic Variations
External Link
Irregular movements
External Link
Advanced Graphical Techniques
External Link
Graphs For Compare Data
External Link
Descriptive Statistics - Measures of Central Tendencies
Introduction to Measures of Central Tendencies
External Link
Mean
External Link
Arithmetic Mean
External Link
Types of Mean
External Link
Median
External Link
Mode
External Link
Skewness & Kurtosis
External Link
Descriptive Statistics - Measure of Dispersion
Introduction to Dispersion
External Link
Variance
External Link
Standard Deviation
External Link
Interpreting the Standard Deviation
External Link
Range
External Link
Data Analysis & Visualization Plots
Introduction to Outliers
External Link
Univariate Outlier Detection
External Link
Graphical Methods for Outlier Detection
External Link
Robust Statistical Methods
External Link
Multivariate Outlier Detection Methods
External Link
Hypothesis Testing for Outlier Detection
External Link
Impact of Outliers on Statistical Analysis
External Link
Distribution Data & its Empirical Formula
Introduction to Distribution of Data
External Link
Introduction Continuous Distribution
External Link
Normal Distribution
External Link
Central Limit Theorem
External Link
Lognormal Distribution
External Link
F Distribution
External Link
Chi-square Distribution
External Link
T - Distribution
External Link
Weibull Distribution
External Link
Limiting Distributions
External Link
Bernoulli Distribution
External Link
Binomial Distribution
External Link
Geometric Distribution
External Link
Uniform Distribution
External Link
Frequency Distributions
External Link
Non grouped Frequency Distributions
External Link
Poisson Distribution
External Link
Inferential Statistics- Hypothesis Testing
Introduction to Hypothesis Testing
External Link
Z-test
External Link
SKT_DS_Theory_SQL for Data Science
52 Learning Materials
Introduction to Databases & RDBMS
Understanding Databases
External Link
Database Management System
External Link
Relational Database
External Link
Non- Relational Database
External Link
History
External Link
Installing SQL
Setting Up MySQL Environment
External Link
Database Processing Paradigms
OLAP vs. OLTP
External Link
Introduction to SQL
SQL Fundamentals and Introduction
External Link
SQL Syntax & Basic Queries
SQL Syntax & Basic Queries
External Link
SQL Data Types
Introduction to Data Types
External Link
Numeric Data Types
External Link
String Data Type
External Link
Date and Time Data Type
External Link
Binary Data Type
External Link
Miscellaneous Data Type
External Link
SQL Operators
Introduction to Operators
External Link
Arithmetic Operators
External Link
Comparison operators
External Link
Logical operators
External Link
Bitwise Operators
External Link
Compound Operators
External Link
String Operators
External Link
Set Operators
External Link
SQL Basics and Language Components
SQL Commands
External Link
Data Definition Language Commands
CREATE Database
External Link
USE Database
External Link
DROP Database
External Link
ALTER Database
External Link
CREATE Table
External Link
RENAME Table
External Link
DROP Table
External Link
TRUNCATE Table
External Link
Data Manipulation Language Commands
Retrieving data from a table
External Link
Inserting data into a table
External Link
Updating existing data into a table
External Link
Deleting all records from a table
External Link
Data Query Language Commands
Introduction to Clauses
External Link
FROM Clause
External Link
WHERE Clause
External Link
WITH Clause
External Link
ORDER BY Clause
External Link
LIMIT Clause
External Link
SQL Functions
Introduction to MYSQL Functions
External Link
Numeric Functions
External Link
String Functions
External Link
Date Time Functions
External Link
Conversion Functions
External Link
User Defined Functions
External Link
SQL GROUP BY , HAVING & Aggregate Functions
SQL Aggregation
External Link
SQL Grouping - GROUP BY
External Link
SQL Grouping - HAVING
External Link
SQL Indexes
Creating Indexes
External Link
SKT_DS_Theory_Power BI
32 Learning Materials
Introduction to Power BI
Introduction to Business Intelligence
External Link
Introduction to Power BI
External Link
History of Power BI
External Link
Comparison of Power BI Version
External Link
Introduction to Building Blocks
External Link
Working with Power BI
Working with Power BI
External Link
Power Pivot and Data Sources
Power Pivot and Data Sources
External Link
Power Query for Data Transformation
Introduction to Power Query
External Link
Transformations
External Link
Data Transformations and Calculations
External Link
DAX
DAX
External Link
Basics of Data Modeling
External Link
Creating Calculated Columns (Basics)
External Link
DAX Time Intelligence
External Link
DAX Table Functions
External Link
Working with Filter Context (FILTER and ALL)
External Link
Overriding Filter Context
External Link
Semi-Additive Measures
External Link
Nested Row Context
External Link
Error Handling
External Link
Building PivotTables on Power Pivot
Creating Tabels And Matrices
External Link
Building Pivot Tables on Top of Power Pivot Tables
External Link
Using Pivot Charts
External Link
Introduction Power View
Introduction to Power View
External Link
Authoring Power View reports
From table to chart
External Link
Working with bubble charts
External Link
Slicers, cards and multiples
External Link
Adding maps to Power View
External Link
Working with multi-view reports
External Link
Improving Power Pivot models for Power View reporting
External Link
Deploying Power View reports
Saving Power View reports in document libraries
External Link
Power Pivot Gallery functionality
External Link
SKT_DS_Theory_ML-Supervised
104 Learning Materials
Course Overview
Curriculum Overview
External Link
Pre-Requisites
External Link
Introduction To Machine Learning
Introduction To Machine Learning
External Link
History of Machine Learning
External Link
Traditional Programming vs Machine Learning
External Link
What can go wrong with machine Learning?
External Link
AI vs ML vs DL
External Link
Data for Machine Learning
External Link
Machine Learning Approaches
External Link
Transfer Learning & Ensemble Learning
External Link
Machine Learning Workflow
Data Collection
External Link
Data Splitting and Preparation
External Link
Model Selection
External Link
Model Training & Evaluation
External Link
Model Optimization and Deployment
External Link
Model Monitoring and Maintenance
External Link
Supervised Learning
Supervised Learning
External Link
Classification & Regression
What is classification?
External Link
Binary Classification
External Link
Case Studies on Binary Classification
External Link
Multiclass classification
External Link
Regression and Case studies
External Link
Algorithms for Supervised Learning
Algorithms for Supervised Learning
External Link
Linear Regression
Introduction to Linear Regression
External Link
Simple Linear Regression
External Link
Case study for Simple Linear Regression (Salary Prediction)
Problem Statement- Salary Prediction
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Data Transformation & Data Splitting
External Link
Model Application
External Link
Introduction to Multiple Linear Regression
Multiple Linear Regression
External Link
Case study for Multiple Regression(Medical Insurance Cost)
Problem Statement-Medical Insurance Cost Prediction
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Data Transformation & Data Splitting
External Link
Model Application
External Link
Introduction to Polynomial Regression
Introduction to Polynomial Regression
External Link
Case study for Polynomial regression (Car Price Prediction)
Problem Statement
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis
External Link
Transformation of data & Data splitting
External Link
Model Application
External Link
Assumptions of linear regression
External Link
Logistic Regression
Logistic Regression
External Link
Geometrical Intuition
External Link
Binary Logistic Regression
External Link
Case study for Binary logistic regression (Ad-click)
Problem Statement
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis
External Link
Transformation of data & Data splitting
External Link
Model Application
External Link
Multinonial Logistic Regression
Introduction to Multinonial Logistic Regression
External Link
Case study for Multinomial logistic regression (iris)
Problem Statement
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis
External Link
Transformation of data & Data splitting
External Link
Model Application
External Link
Assumptions of logistic regression
External Link
Naive Bayes
Introduction to Naive Bayes
External Link
Exploring Naive Bayes
External Link
NaiveBayes - Classifier & Regressor
External Link
Bayes Theorem
External Link
Types of Naive Bayes
External Link
Multinomial Naive Bayes
External Link
Bernoulli Naive Bayes
External Link
Parameter Estimation
External Link
Statistical Estimation Techniques
External Link
Smoothing techniques
External Link
Case Study on Naive Bayes(Heart Attack Prediction)
Problem Statement_Heart Attack Prediction
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis(EDA)
External Link
Data Transformation
External Link
Feature selection & Data splitting
External Link
Model Application
External Link
KNN Algorithm
Introduction to KNN Algorithm
External Link
Exploring KNN Algorithm
External Link
Distance Metrics
External Link
Types of KNN Algorithms
External Link
KNN for Regression (California House price prediction)
Problem Statement- Housing Prices Prediction in California
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Data Splitting & Data Transformation
External Link
Model Application
External Link
KNN for Classification (Diabetes Prediction)
Problem Statement- Diabetes Prediction
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning & Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Data Splitting & Data Transformation
External Link
Model Application
External Link
SKT_DS_Lab L1_ML-supervised
1 Learning Materials
Basic level Program On Case study for simple linear regression
Apply a linear regression model so that Y is predicted based on X
External Link
Basic level Program On Case study for Multiple Linear regression
Basic level Program On Case study for Polynomial regression
Basic level Program On Case study for Binary logistic regression Basic level Program On
Basic level Program On Case study for Multinomial logistic regression
SKT_DS_Lab L2_ML-Supervised
1 Learning Materials
Intermediate Level Programming on Case study for Simple linear Regression
Analyze the diabetes dataset from scikit-learn of simple linear regression to predict
External Link
Intermediate Level Programming on Case study for Multiple regression
Intermediate Level Programming on Case study for Polynomial regression
Intermediate Level Programming on Case study for Binary logistic regression
Intermediate Level Programming on Case study for Multinomial logistic regression
SKT_DS_Lab L3_ML-Supervised
1 Learning Materials
Advance Level Programming on Case study for simple linear regression
Create a dataset with 1000 samples and 20 features using `sklearn.datasets.
External Link
SKT_DS_Theory_ML-Unsupervised
81 Learning Materials
Unsupervised Learning
Introduction to Unsupervised Learning
External Link
Types of Unsupervised Learning
External Link
Clustering
Introduction to Clustering
External Link
Types of clustering
External Link
Hierarchical Clustering
Hierarchical Clustering
External Link
Types of hierarchical clustering
External Link
Agglomerative Hierarchical Clustering
Agglomerative Hierarchical Clustering
External Link
Applications of Agglomerative Hierarchical Clustering
External Link
Distance measures
External Link
Linkage criteria
External Link
Dendrograms
External Link
Divisive Hierarchical Clustering
Divisive Hierarchical Clustering
External Link
Applications of Divisive Hierarchical Clustering
External Link
Comparing Agglomerative and Divisive Hierarchical Clustering
Comparing Agglomerative and Divisive Hierarchical Clustering
External Link
Case Study on Hierarchical Clustering
Problem Statement
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning and Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Implementing Hierarchical Clustering
External Link
K-Means Clustering
Introduction to K-Means Clustering
External Link
Applications of K-Means Clustering
External Link
Understanding K-means Clustering
External Link
Evaluating K-means Clustering-Using Silhouette Method
External Link
Evaluating K-means Clustering-Using Elbow Method
External Link
Evaluating K-means Clustering – Using the Gap Statistic
External Link
Advantages and Disadvantages Clustering Methods
External Link
Limitations of K-Means Clustering
External Link
Case Study on K-Means Clustering
Problem Statement
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning and Preprocessing
External Link
Exploratory Data Analysis (EDA) part-1
External Link
Exploratory Data Analysis (EDA) part-2
External Link
Implementing K-means Clustering
External Link
DBSCAN Algorithm
Introduction to DBSCAN Algorithm
External Link
Understanding DBSCAN Algorithm
External Link
Advantages and Disadvantages of DBSCAN
External Link
Case Study on DBSCAN Algorithm
Problem Statement
External Link
Data Collection
External Link
Data Understanding
External Link
Data Cleaning and Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Implementing DBSCAN Algorithm
External Link
Mean Shift clustering
Introduction to Mean Shift clustering
External Link
Understanding Density Estimation Techniques
External Link
Advantages and Disadvantages
External Link
Implementation of Mean Shift Clustering
Implementing Mean Shift clustering
External Link
Dimensionality Reduction Algorithm
Introduction to Dimensionality Reduction Algorithm
External Link
Types of dimensionality reduction techniques
External Link
Principal Component Analysis (PCA)
External Link
T-Distributed Stochastic Neighbor Embedding
External Link
Non-Negative Matrix Factrorization (NMF)
External Link
Independent Component Analysis(ICA)
External Link
Linear Discriminant Analysis (LDA)
External Link
Gaussian Mixture Models (GMM)
Introduction to Gaussian Mixture Models (GMM)
External Link
Case Study on Gaussian Mixture Models (GMM)
Data Collection
External Link
Data Understanding
External Link
Data Cleaning and Preprocessing
External Link
Implementing Gaussian Mixture Models
External Link
Association Rule Learning
Introduction to Association Rule Mining
External Link
Apriori Algorithm
External Link
FP-growth algorithm
External Link
Eclat Algorithm
External Link
Case Study on Association Rule Mining
Data Collection
External Link
Data Understanding
External Link
Data Cleaning and Preprocessing
External Link
Exploratory Data Analysis (EDA)
External Link
Implementing Association Rule Mining
External Link
Anomaly Detection
Introduction to Anomaly Detection
External Link
Unsupervised Anomaly Detection Techniques
External Link
Real-world Applications
External Link
Recommendation systems
Introduction to Recommendation Systems
External Link
Content-Based Recommendation Systems
External Link
Collaborative Recommendation Systems
External Link
Hybrid Recommendation Systems
External Link
Case Study on Recommendation systems
Data Collection
External Link
Data Understanding
External Link
Data Cleaning
External Link
Data Preprocessing
External Link
Implementing Recommendation Systems
External Link
SKT_DS_Lab L1_ML-Unsupervised
1 Learning Materials
Basic level Program On Hierarchical Clustering
Implement Hierarchical Clustering on a small dataset.
External Link
Basic level Program On K-Means Clustering
Basic level Program On Case Study on DBSCAN Algorithm
Basic level Program On Gaussian Mixture Models (GMM)
Basic level Program On Association Rule Mining
SKT_DS_Lab L2_ML-Unsupervised
1 Learning Materials
Intermediate Level Programming on Hierarchical Clustering
Compare Hierarchical Clustering with DBSCAN on noisy data.
External Link
Intermediate level Program on K-Means Clustering
Intermediate Level Programming on DBSCAN Algorithm
Intermediate Level Programming on Gaussian Mixture Models (GMM)
Intermediate Level Programming on Association Rule Mining
SKT_DS_Lab L3_ML-Unsupervised
1 Learning Materials
Advance Level Programming on Hierarchical Clustering
Perform Hierarchical Clustering on a large dataset (e.g., customer segmentation)
External Link
Advance Level Programming on K-Means Clustering
Advance Level Programming on DBSCAN Algorithm
Advance Level Programming on Gaussian Mixture Models (GMM)
Advance Level Programming on Association Rule Mining
SKT_DS_Theory_Artificial Intelligence
36 Learning Materials
Introduction to AI
Introduction to Artificial Intelligence
External Link
History of Artificial Intelligence
External Link
Types of Artificial Intelligence
External Link
Applications of Artificial Intelligence
External Link
Intelligent Agents
Introduction of agents
External Link
Agent Architectures
External Link
Types of Agents
External Link
Types of Environment Agents
External Link
Problem solving by Searching
State space Representation
External Link
search strategies
External Link
evaluation of search strategies
External Link
Informed Search
Heuristic Function
External Link
Gredy best first search
External Link
A* Algorithm
External Link
Simulated Annealing search
External Link
Game Playing
Introduction to game playing
External Link
Monte Carlo Tree Search (MCTS)
External Link
Wumpus World Problem
External Link
Game Playing solving examples
Tower of Hanoi
External Link
Travelling Salesman Problem
External Link
Knowledge Representation
Introduction to Knowledge Representation
External Link
Approaches to Knowledge Representation
External Link
Techniques in Knowledge Representation
External Link
predicate logic
External Link
knowledge Inference
Introduction To Knowledge Inference
External Link
backward chaining and forward chaining
External Link
fuzzy reasoning - certainity factors
External Link
dempster shafer theory
External Link
Structured Knowledge representation
Representation and Mappings
External Link
semantic nets , frames
External Link
conceptual dependencies and scripts
External Link
Expert Systems
Introduction to Expert System
External Link
How expert systems work
External Link
Types of expert systems
External Link
Typical expert systems
External Link
Expert systems shells
External Link
SKT_DS_Lab L1_Artificial Intelligence
1 Learning Materials
Basic Level Programming on Knowledge Representation
predicates One converts centigrade temperatures to Fahrenheit
External Link
Basic Level Programming on knowledge Inference
Basic Level Programming on structured Knowledge representation
Basic Level Programming on Expert Systems
SKT_DS_Lab L2_Artificial Intelligence
1 Learning Materials
Intermediate Level Programming on Informed Search
To write a Lisp Program to implement the STEEPEST-ASCENT HILL CLIMBING.
External Link
Intermediate Level Programming on Game Playing
Intermediate Level Programming on Knowledge Representation
Intermediate Level Programming on knowledge Inference
Intermediate Level Programming on structured Knowledge representation
SKT_DS_Lab L3_Artificial Intelligence
2 Learning Materials
Advance Level Programming on Problem solving by Searching
Implement Farmer Crosses River Puzzle
External Link
Advance Level Programming on Knowledge Representation
Evaluating Propositional Logic Statements
External Link
Advance Level Programming on knowledge Inference
Advance Level Programming on structured Knowledge representation
Advance Level Programming on Expert Systems
SKT_DS_Theory_Deep Learning
73 Learning Materials
Introduction to AI
What is AI?
External Link
Brief history of AI
External Link
AI Techniques
External Link
Introduction to Deep learning
Introduction to Deep learning
External Link
Applications of Deep learning
Deep learning in Recommender Systems
External Link
Deep learning in Health care
External Link
Deep Learning In Finance
External Link
Deep Learning in Robotics
External Link
Lifecycle of deep learning
Problem Definition
External Link
Data Collection
External Link
Data preprocessing
External Link
Model Selection
External Link
Training and Evaluation
External Link
Fine-Tuning and Deployment
External Link
Monitoring and Maintenance
External Link
About Keras and TensorFlow
About Keras and TensorFlow
External Link
Neural Network
Introduction To Neural Network
External Link
Layers in Neural Networks
External Link
Perceptrons
Introduction to Perceptron
External Link
Perceptron For Binary Classification
External Link
Multi Layer Perceptron
External Link
Logistic Regression vs Perceptron
External Link
Diagrammatic Representation
External Link
Activation Functions
Activation Functions
External Link
Sigmoid Activation Functions
External Link
Hyperbolic Tangent Activation Functions
External Link
RELU Activation Functions
External Link
Leaky RELU Activation Function
External Link
PRELU Activation Function
External Link
Swish Activation Function
External Link
Softmax Activation Function
External Link
Types of Neural Networks
Types of Neural Networks
External Link
Feedforward Neural Networks
External Link
Convolutional Neural Networks
External Link
Recurrent Neural Networks
External Link
Generative Adversarial Networks
External Link
Autoencoders
External Link
Artificial Neural Networks
Vanishing Gradient
External Link
Exploding Gradients
External Link
Forward Propagation
External Link
Back Propagation Algorithm
External Link
Error computation
External Link
Training an Artificial Neural Network
The Universal Approximation Theorem
External Link
ANN Preprocessing Techniques
External Link
Data Cleaning
External Link
Data Integration
External Link
Data Transformation
External Link
Data Reduction
External Link
Optimization Methods for ANN Training
Local Minima and Global Minima
External Link
Optimization Algorithms in Neural Network
External Link
Gradient Descent Optimizer
External Link
Stochastic Gradient Descent Optimizer
External Link
Mini Batch Stochastic Gradient Descent
External Link
Stochastic Gradient Descent with Momentum
External Link
AdaGrad optimizer
External Link
RMSprop Optimizer
External Link
Adam Optimizer
External Link
AdaDelta Optimizer
External Link
Overfitting and Regularization Techniques
Overfitting In Neural Networks
External Link
Early stopping
External Link
Dropout method
External Link
Regularization techniques to prevent Overfitting
External Link
Employee Attrition Classification case study
Problem Statement
External Link
Data Collection
External Link
Data Understanding
External Link
Data cleaning and preprocessing
External Link
Univariate Analysis
External Link
Exploratory data Analysis-Bivariate Analysis(part1)
External Link
Exploratory data Analysis-Bivariate Analysis(part 2)
External Link
Exploratory data Analysis Multivariate Analysis
External Link
Feature Selection
External Link
Implementing ANN model(part 1)
External Link
Implementing ANN model(part 2)
External Link
SKT_DS_Lab L1_Deep Learning
3 Learning Materials
Basic Level Programming on About Keras and TensorFlow
To study various tools: Torch, TensorFlow, Keras
External Link
Basic Level Programming on Perceptrons
Write a program to implement XOR gates using Perceptron.
External Link
Basic Level Programming on Activation Functions
Program a simple neural network perform sentiment analysis on txt data with Softmax Activation
External Link
Basic Level Programming on Types of Neural Networks
Basic Level Programming on Artificial Neural Networks
SKT_DS_Lab L2_Deep Learning
1 Learning Materials
Intermediate Level Programming on Activation Functions
Write a Python program to plot a few activation functions that are being used in neural networks
External Link
Intermediate Level Programming on Types of Neural Networks
Intermediate Level Programming on Artificial Neural Networks
Intermediate Level Programming on Training an Artificial Neural Network
Intermediate Level Programming on Optimization Methods for ANN Training
SKT_DS_Lab L3_Deep Learning
3 Learning Materials
Advance Level Programming on Perceptrons
With a suitable example demonstrate the perceptron learning law using python
External Link
Advance Level Programming on Activation Functions
write a program uses a simple neural network to predict the air quality index ReLU function
External Link
Advance Level Programming on Types of Neural Networks
write a program uses a simple neural network to predict the air quality index ReLU function
External Link
Advance Level Programming on Artificial Neural Networks
Advance Level Programming on Training an Artificial Neural Network
SKT_DS_Theory_Computer Vision
88 Learning Materials
Introduction to computer vision
Introduction to Computer Vision
External Link
History of computer vision
External Link
Applications used for computer vision
Computer Vision in Healthcare
External Link
Computer Vision in Agriculture
External Link
Computer Vision in Manufacturing
External Link
Computer Vision in Transportation
External Link
Computer Vision in Retail
External Link
Convolutional Neural Networks
Basics of CNN
External Link
The Convolutional Layer
External Link
padding,strides and channels in CNN
External Link
Pooling Layers
External Link
The fully connected layers
External Link
CNN Architecture
External Link
Learnable Parameters in CNN
External Link
Image classification using CNN
External Link
Transfer Learning
External Link
CNN Case Study
The Problem Statement
External Link
Model Building
External Link
Conclusion
External Link
Basics of Digital Image Processing
Introduction to Digital Image Processing
External Link
Origins of Digital Image Processing
External Link
Example Fileds that uses digital Image Processing
External Link
Fundamental steps in digital image processing
External Link
Components of Digital Image Processing
External Link
Elements of Visual perception
External Link
Image Sensing and Acquisition
External Link
Image Sampling and Quantization
External Link
Digital Image Representation
External Link
What is Image?
External Link
Basic Relationships Between Pixels
External Link
Object detection
Introduction to Object Detection
External Link
Image Filtering
External Link
Feature Extraction
External Link
Edge Detection
External Link
Feature-based Methods(SIFT, SURF)
External Link
Histogram of Oriented Gradients
External Link
Evaluation Metrics for Object Detection
External Link
Anchor Boxes and Aspect Ratios
External Link
Data Augmentation Techniques
External Link
YOLO Algorithm
External Link
Single Shot Detectors
External Link
Region-based Approaches(R-CNN,FAST R-CNN, FASTER R-CNN)
External Link
Object Detection Case study
The Problem Statement
External Link
Model Building
External Link
Conclusion
External Link
Image Segmentation
Image segmentation
External Link
Image segmentation Architectures
External Link
Image segmentation Loss Functions
External Link
Image segmentation Frameworks
External Link
Image Segmentation Datasets
External Link
Video Segmentation
External Link
Real World Use Cases of Image Segmentation
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Similarity Learning
Introduction to Similarity Learning
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Siamese Networks
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Triplet Loss
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Contrastive Learning
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Metric Learning
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Embedding Spaces
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Image Captioning
Introduction to Image Captioning
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Model Architectures
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Attention Mechanisms
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Multimodal Learning
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Evaluation Metrics
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Deep Learning Approaches on Image Captioning
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Image Transformation
Geometric Transformations
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Color Space Transformations
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Frequency Domain Transformations
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Image Restoration and Reconstruction
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Transformations for compression
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Generative Models
Introduction To Generative Models
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Types of Generative Models
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Mathematical Foundations
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Normalizing Flows
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Applications of Generative Models
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Video Classification
Introduction to Video Classification
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Techniques and Algorithms
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Data Preparation and Preprocessing
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Challenges and Solutions in Video Classification
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Extending Image-based Approaches to Videos
Extending Image-based Approaches to Videos
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Autoencoders
Introduction to Autoencoders
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Types of Autoencoders
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Variational Autoencoders
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Loss Functions and Training Techniques
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Applications of Autoencoders
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Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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Markov Decision Process
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Bellman Equation
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Q-Learning
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SKT_DS_Lab L1_Computer Vision
3 Learning Materials
Basic Level Programming on Convolutional Neural Networks
Build a Convolution Neural Network for MNIST Hand written Digit Classification
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Basic Level Programming on Basics of Digital Image Processing
To acquire an image, store in different formats and display the properties of the images
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Basic Level Programming on Object detection
program that enhances grayscale wildlife images captured under low-light conditions
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Basic Level Programming on Image Segmentation
Basic Level Programming on Image Captioning
SKT_DS_Lab L2_Computer Vision
3 Learning Materials
Intermediate Level Programming on CNN
convolutional neural networks
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Intermediate Level Programming on Basics of Digital Image Processing
Develop a wavelet-based image compression method using Discrete Wavelet Transform (DWT)
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Program that takes both color and grayscale images as input and applies blurring in a smart way
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Intermediate Level Programming on Object detection
Intermediate Level Programming on Image Segmentation
Intermediate Level Programming on Image Captioning
SKT_DS_Lab L3_Computer Vision
3 Learning Materials
Advance Level Programming on CNN
Build a Convolution Neural Network for simple image (dogs and Cats) Classification
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Advance Level Programming on Basics of Digital Image Processing
Simple content based image retrieval using various distance metrics
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develop a smart and efficient way to compress USING Image sampling and quantization
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Advance Level Programming on Object Detection
Advance Level Programming on Image Segmentation
Advance Level Programming on Similarity Learning
SKT_DS_Theory_NLP
89 Learning Materials
Introduction to Natural Language Processing
What is Language?
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What is Natural Language Processing?
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History of Natural Language Processing
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Email Plaform
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Voice based Assistants
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Modern Search Engines
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Chat Support
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Machine Translation Services
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NLP Tasks
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Approaches in NLP
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What makes Natural Language Processing Difficult?
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Libraries for Deep Learning in NLP
NLP Libraries
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Text Wrangling
Tokenization
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Stemming
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Lemmatization
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Stopword Removal
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Rare word removal
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Spell correction
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Replacing Synonyms
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Replacing negations with antonyms
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Word Vector Represenations
Introduction to Word Embedding
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Word2Vec
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Bag-of-Words
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Topic Modeling
Topic Modeling
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Non-Negative Matrix Factorization (NMF)
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Parts of Speech Tagging
Introduction to Parts-of-Speech Tagging
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Training a unigram part-of-speech tagger
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Training & combining Tri & ngram tagger
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Creating a model of likely words tags
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Corpora
Introduction to Corpora
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Setting up a custom corpus
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Creating a wordlist corpus
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Transforming Chunks and Trees
Filtering insignificant words from a sentence
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Correcting verb forms
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Swapping verb phrases
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Swapping noun cardinals
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Swapping infinitive phrases
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Singularizing plural nouns
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Chaining chunk transformations
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Converting a chunk tree to text
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Flattening a deep tree
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Converting tree labels
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Extracting Chunks
Chunking and chinking with regular expressions
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Merging and splitting chunks with regular expressions
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Partial parsing with regular expressions
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Training a tagger-based chunker
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Classification-based chunking
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Training a named entity chunker
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Extracting proper noun chunks
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Extracting location chunks
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Extracting named entities
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Training a Named Entity Chunker
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Training a Chunker with NLTK-Trainer
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Text classification with machine learning
Introduction to text classification
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Multinomial Naive Bayes Classifier
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KNN classifier
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Sentence Classification with Convolutional Neural Networks
Introducing Convolution Neural Networks
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The Convolutional Layer
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The pooling and subsampling Layers
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The Fully Connected Layers
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Recurrent Neural Networks
Recurrent Neural Networks
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Backpropagation Through Time
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Applications of RNNs
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Generating text with RNNs
Attention Mechanisms and Their Integration with RNNs
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Sequence-to-Sequence Models
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Bidirectional RNNs
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Transfer Learning with RNNs
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Challenges with Advanced RNNs
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Future Directions of RNNs
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Hybrid Models: Combining RNNs with CNNs
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Reinforcement Learning with RNNs
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Ethical Considerations and Bias in RNNs
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Recurrent Neural Networks with Context Features – RNNs with longer memory
Memory-Augmented Neural Networks (MANNs)
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Challenges in RNN Memory
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Future Directions in RNN Memory
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Long Short-Term Memory Networks
How LSTMs solve the vanishing gradient problem
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Variants of LSTMs
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Applications of LSTM
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Generating Text with LSTM
Text Generation with LSTMs
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Building a Character-Level Language Model
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Training the LSTM Model for Text Generation
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Comparing LSTMs to LSTMs with peephole connections and GRUs
Standard LSTM
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Speech Recognition
Speech Recognition
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Statistical Speech Recognition
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Error Metrics
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Transfer Learning: Scenarios, Self-Taught Learning, and Multitask Learning
Transfer Learning
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Self-Taught Learning
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Multitask Learning
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Zero-Shot, One-Shot, and Few-Shot Learning
One-Shot Learning
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