Introduction to machine learning concepts, supervised and unsupervised learning paradigms, and model evaluation metrics.

Dive into neural networks, backpropagation, convolutional neural networks (CNNs) for image recognition, and recurrent neural networks (RNNs) for sequential data.

Explore fundamental statistical concepts for machine learning, including probability, linear regression, and hypothesis testing.

Understand gradient descent algorithms and explore advanced optimization techniques for deep learning models.

Master essential linear algebra concepts for understanding machine learning algorithms.

Learn techniques for processing and analyzing text data, including sentiment analysis and text summarization.

Learn decision trees, support vector machines, random forests, and other popular supervised learning algorithms.

Explore techniques for analyzing and extracting information from images and videos.

Explore techniques like k-means clustering, principal component analysis (PCA), and anomaly detection.

Understand the principles of reinforcement learning and its applications in areas like robotics and game playing.

Learn about ethical considerations, bias mitigation techniques, and explainable AI (XAI) methods

Apply your learned skills to a real-world machine learning project, developing and deploying a working model to address a specific challenge.