Syllabus

Syllabus we covered

Module 1

Introduction to Machine Learning

Overview of Machine Learning

Definition and types of machine learning (supervised, unsupervised, reinforcement)
Applications of machine learning

Machine Learning Workflow

Steps in a machine learning project
Introduction to Python and relevant libraries (NumPy, pandas, scikit-learn)

Module 2

Data Preprocessing

Data Cleaning

Handling missing values
Dealing with outliers
Data normalization and standardization

Feature Engineering

Feature selection and extraction
Encoding categorical variables
Splitting data into training and testing sets

Module 3

Supervised Learning - Regression

Linear Regression

Simple and multiple linear regression
Evaluation metrics: MAE, MSE, RMSE, R^2

Advanced Regression Techniques

Polynomial regression
Regularization methods: Ridge and Lasso regression

Module 4

Supervised Learning - Classification

Logistic Regression

Binary classification
Evaluation metrics: accuracy, precision, recall, F1 score, ROC-AUC

Decision Trees and Random Forests

Building and visualizing decision trees++
Ensemble methods: bagging, boosting, and random forests

Module 5

Supervised Learning - Advanced Classification

Support Vector Machines (SVM)

Linear and non-linear SVM
Kernel tricks

k-Nearest Neighbors (k-NN) and Naive Bayes

k-NN algorithm and distance metrics
Naive Bayes classifier and probability theory

Module 6

Unsupervised Learning - Clustering

k-Means Clustering

Algorithm and implementation
Choosing the number of clusters

Hierarchical Clustering

Agglomerative and divisive clustering
Dendrograms

Module 7

Unsupervised Learning - Dimensionality Reduction

Principal Component Analysis (PCA)

Concept and mathematical foundation
Implementation and visualization

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Non-linear dimensionality reduction
Applications and visualization

Module 8

Model Evaluation and Tuning

Model Evaluation Techniques

Cross-validation
Confusion matrix

Hyperparameter Tuning

Grid search and random search
Using validation curves and learning curves

Module 9

Neural Networks and Deep Learning

Introduction to Neural Networks

Perceptron and multi-layer perceptrons (MLP)
Backpropagation and gradient descent

Deep Learning Basics

Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)

Module 10

Advanced Topics and Final Project

Advanced Machine Learning Techniques

Ensemble learning: boosting, bagging, and stacking
Introduction to reinforcement learning

Final Project Presentation

Students present their final projects
Feedback and discussion
Assessment and Evaluation
Module-based assignments and quizzes
Mid-course project
Final project and presentation