This course covers preliminaries of machine learning required for under graduate students. The following content will be covered in the course:

1. Module 1: 

   • Introduction to Machine Learning - Introduction, Types of Machine Learning, Applications of Machine Learning, Issues in Machine Learning.

   • Preparing to Model- Introduction, Machine Learning Activities, Basic Types of Data in Machine Learning, Exploring Structure of Data, Data Quality and Remediation, Data Pre-Processing

2. Module 2: 

   • Modelling & Evaluation- Introduction, selecting a Model, training a Model (for Supervised Learning), Model Representation and Interpretability, Evaluating Performance of a Model.

   • Basics of Feature Engineering- Introduction, Feature Transformation – Feature Construction, Feature Extraction, Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Linear Discriminant Analysis (LDA), Feature Subset Selection

3. Module 3:

   • Introduction to regression analysis, Simple linear regression, Multiple linear regression, Assumptions in Regression Analysis, Main Problems in Regression Analysis, Improving Accuracy of the linear regression model, Polynomial Regression Model, Logistic Regression, Regularization, Regularized Linear Regression, Regularized Logistic Regression.

4. Module 4: 

   • Classification-Introduction, Example of Supervised Learning, Classification Model, Classification Learning Steps.

   • Common Classification Algorithms - k-Nearest Neighbour (kNN), Support vector Machines (SVM), Random Forest model.

5. Module 5: 

   • Ensemble Learning- Bagging, Boosting, Stacking and its impact on bias and variance, AdaBoost,Gradient Boosting Machines, XGBoost. Reinforcement Learning - Introduction, Q Learning.

3 Building Convolutional Neural Network for MNIST dataset

4 Image classification using CIFAR10 Dataset

5 Image classification using CAT vs DOG dataset

6 LSTM Exercise

7 Word2Vec Exercise