Course curriculum

  • 1

    ML_E-Learning Content

    • Chapter 1 - Introduction to Machine Learning

    • Applications of Machine Learning

    • Course Overview

    • Getting Started with the Course

    • Chapter 2 - Python- Introduction to Python

    • Software Installation

    • First Program

    • Comments

    • Data Types Part - 1

    • Data Types Part - 2

    • Operators Part - 1

    • Operators Part - 2

    • Variables Part - 1

    • Variables Part - 2

    • Syntax

    • Working with Numbers

    • Working with Strings Part - 1

    • Working with Strings Part - 2

    • Date & Time

    • Conditional Statements Part - 1

    • Conditional Statements Part - 2

    • For Loop Part - 1

    • For Loop Part - 2

    • While Loop

    • Lists Tuples Sets Part - 1

    • Lists Tuples Sets Part - 2

    • Lists Tuples Sets Part - 3

    • Dictionaries Part - 1

    • Dictionaries Part - 2

    • Functions Part - 1

    • Function Part - 2

    • Numpy Part - 1

    • Numpy Part - 2

    • Numpy Part - 3

    • MatPlotLib Part - 1

    • MatPlotLib Part - 2

    • Pandas Part - 1

    • Pandas Part - 2

    • Pandas Part - 3

    • Chapter 3 - Statistics- Introduction to Statistics

    • Descriptive Statistics Part - 1

    • Descriptive Statistics Part - 2

    • Measure of Spread

    • Probability

    • Conditional Probability

    • Probability Distributions

    • Hypothesis Testing

    • Chapter 4 - Machine Learning Algorithms -Deep Dive into ML Part - 1

    • Deep Dive into ML Part - 2

    • Linear Regression

    • Logistic Regression

    • KNN - K Nearest Neighbours

    • Naive bayes

    • Regression VS Classification

    • SVM - Support Vector Machines

    • Decision Tree

    • Clustering

    • K-means Clustering

    • HC - Hierarchical Clustering

    • DBSCAN

    • Dimensionality Reduction

    • Principle Component Analysis

    • Linear Discriminant Analysis

    • Recommendation Systems

    • Collaborative Filtering System

    • Content Based System

    • Hybrid Recommendation System

    • Supervised VS Unsupervised

    • Semi-Supervised Learning

    • Reinforcement Learning Part - 1

    • Reinforcement Learning Part - 2
  • 2

    ML_Restaurant Review using NLP from Fox Trading_Project 1

    • Restaurant Review using NLP

    • NLP IPYNB File

    • Restaurant Reviews TSV File
  • 3

    ML_Wireless Sound Control from Fox Trading_Project 2

    • Wireless Sound Control

    • Wireless Sound Control PYTHON File