Machine Learning

B.Tech Course Scheme

• B.Tech IT Sem 6 Scheme

• Machine Learning UG Syllabus PDF

Course Materials (PPTs/PDFs/Handouts):

• 1. Introduction

     • Introduction to Machine Learning PDF

     • Evaluation of Learning Algorithms PDF

       • Model Evaluation (Regularization) - 25 Jan 2025 PDF

       • Model Improvement (Ensemble Learning) - 25 Jan 2025 PDF

       • Model Optimization - 25 Jan 2025 PDF

       • Confusion Matrix  - Jan 2025 PDF  Spreadsheet

     • Inferential Statistics: Hypotheses Testing LaTeX PDF

     • Steps for developing a ML Application Google Colab

     • Training Dataset vs Testing Dataset vs Validation Dataset Goolge Colab

     • Trade-off between Bias and Variance [ PDF ]

  2. Learning with Regression and Trees

     • Learning with Regression

       • Simple Linear Regression PDF

         • Linear Regression Example 1 PDF

         • Linear Regression Example 2 PDF

       • Multiple Linear Regression PDF

         • Multiple Regration Solved Example PDF

       • Logistic Regression PDF

         • Role of Log Odds in Logistic Regression PDF

         • Logistic Regression Solved Example 1 [ PDF ]

         • Logistic Regression Solved Example 2 [ PDF ]

     • Learning with Trees

       • Decision Trees - Classification PDF

       • Decision Trees - Regression PDF

       • Classification and Regression Trees (CART) PDF

         • CART Solved Example [ PDF ]

  3. Dimensionality Reduction

     • Principal Component Analysis (PCA) PDF

       • PCA Solved Example 1 PDF

       • PCA Solved Example 2 PDF

     • Independent Component Analysis (ICA) PDF

       • ICA Solved Example [ PDF ]

     • Singular Value Decompostion (SVD) PDF | 

       • SVD Solved Example 1 PDF

       • SVD Solved Example 2 PDF

       • SVD Solved Example 4 PDF

       • SVD Solved Example 5 PDF

  4. Classification

     • Bayesian Belief Networks (BBN) PDF

       • BBN Solved Example PDF

     • Hidden Markov Models (HMM) PDF

       • HMM Solved Example [ PDF ]

     • Support Vector Machine PDF

       • SVM Solved Example PDF

     • K-Nearest Neighbor PDF

       • KNN Solved Example PDF

  5. Clustering

     • Introduction Clustering

     • Density Based Clustering: DBSCAN [ PDF ]

     • Expectation Maximization (EM) [ PDF ]

     • Radial Basis functions [ PDF ]

       • RBF Solved Example [ PDF ]

  6. Applications of Machine Learning

     • Recommender Systems [ PDF ]

     • Image Recognition [ PDF ]

     • Sentiment Analysis  [ PDF ]

     • Machine Learning for Video Surveillance  [ PDF ]

💻  💻 💻  💻 💻  💻 💻  💻 💻  💻 💻  💻 

Lab Experiments

• Guidelines for writing DL Lab Experiments - Jan 2023 [ PDF ]

• List of Datasets: 

  • insurance.csv

  • heart.csv

  • iris.csv

  • kerala.csv

1. Introduction to Machine Learning

   • EDA on Insurance Dataset  [  ipython ] [ insurance.csv ]

   • Training Dataset vs Testing Dataset vs Validation Dataset [ ipython ]

   • Implement Machine Learning Steps in Python  [  ipython ]

   • Data Visualization Graph and Plots  [  ipython ]

   • Basics of Python   [  ipython ]

2. Learning with Regression and Trees

   • To implement Linear Regression

   • To implement Logistic Regression 

     • Heart Disease Prediction using Logistic Regression  [  ipython ]

   • Decision Tree Learning

     • Decision Tree Learning using iris Dataset   [  ipython ]

     • Kerala Flood Prediction using Decision Tree   [  ipython ]

   • To implement CART decision tree algorithm

3. Dimensionality Reduction

   • Dimensionality Reduction using PCA [ ipython ]

   • Dimensionality Reduction using SVD [ ipython ]

   • Implementation of Singular Value Decomposition (SVD) [ ipython ]

4. Classification

   • To implement Support Vector Machine

     • Heart Disease Prediction using SVM  [  ipython ]

     • Breast Cancer Prediction using SVM Classifier [ ipython ]

   • To implement Bayesian Classification

   • To implement Hidden Hidden Markov Model

     • Weather Prediction using HMM [ ipython ]

     • Speech recognition using HMM  [ ipython ]

   • To Implement Random Forest

     • Heart Disease Prediction using Random Forest  [  ipython ]

   • To implement K-Nearest Neighbour

     • Heart Disease Prediction k-NN Classifier  [  ipython ]

5. Clustering

   • Density-Based Clustering: DBSCAN [ ipython ]

   • To implement Radial basis functions

6. Applications of Machine Learning

   • Mini project based on any machine learning application

🏁🏁🏁🏁🏁🏁🏁🏁🏁🏁🏁🏁

Lab Manual

• Sample Gray Scale 8-bit BMP Images for experiment [ IMAGE FOLDER ]

• Guidelines for writing DL Lab Experiments - Jan 2023 [ PDF ]

• Machine Learning using Python = Lab Manual - July 2020 [ PDF ]