Overview of Machine Learning and its role in data analysis. Historical context and evolution of Machine Learning. Key components of Machine Learning systems.

Overview of supervised learning and its applications. Regression analysis: simple and multiple linear regression. Classification algorithms: logistic regression, decision trees, and k-Nearest Neighbors.

Overview of unsupervised learning and its applications. Clustering algorithms: K-means, DBSCAN, and hierarchical clustering. Dimensionality reduction techniques: Principal Component Analysis (PCA) and t-SNE.

Overview of neural networks and their applications. Artificial Neural Networks (ANNs) and their architecture. Training neural networks using backpropagation.

Overview of deep learning and its applications. Convolutional Neural Networks (CNNs) for image recognition. Recurrent Neural Networks (RNNs) for sequence data.