This module introduces foundational machine learning algorithms, including perceptron, KNN, decision trees, and Naïve Bayes. Learners will gain hands-on experience with these simple learners, understand their underlying principles, and explore practical implementation using Python and Scikit-learn.

This module introduces the concepts of similarity in machine learning, focusing on how algorithms like K-means clustering and K-Nearest Neighbors (KNN) use distance metrics to group and classify data. Learners will explore the assumptions behind these algorithms, experiment with tuning and convergence, and understand practical implementation considerations.

This module introduces the fundamentals of linear and logistic regression for prediction and classification tasks. Learners will explore feature selection, model evaluation metrics like R-squared and RMSE, regularization techniques, and optimization using stochastic gradient descent. Practical strategies for handling different data types and multiclass problems are also covered.

This module delves into advanced support vector machine (SVM) techniques, focusing on handling nonseparable data with kernel methods and mathematical optimization. Learners will explore the theoretical foundations of SVMs, their practical applications in fields like image recognition and language processing, and how to implement them using Python libraries.

This module introduces the foundational concepts of neural networks, including their architecture, learning processes, and practical applications in deep learning. Learners will explore feed-forward and backpropagation mechanisms, understand challenges like overfitting, and gain hands-on experience with frameworks such as Keras. The module also covers advanced architectures like convolutional and recurrent neural networks.

This module introduces ensemble learning methods, including Bagging, Random Forests, and Boosting, to enhance predictive performance in machine learning. Learners will compare popular algorithms like XGBoost, LightGBM, and CatBoost, and explore how combining multiple models can reduce overfitting and improve accuracy on tabular data.