Machine learning is transforming industries, and mastering its core algorithms is essential for AI-driven innovation. This course provides a structured approach, starting with fundamental concepts and Python implementation. You’ll explore supervised learning models, from linear regression to advanced ensemble techniques like random forests and gradient boosting. Each algorithm is implemented from scratch, reinforcing both theory and hands-on coding skills. Beyond supervised learning, you’ll dive into unsupervised techniques such as clustering and dimensionality reduction. Implement K-Means, DBSCAN, autoencoders, and t-SNE to uncover hidden patterns in data. The course also introduces reinforcement learning and deep learning, covering Q-learning, policy gradients, CNNs, RNNs, and transformers—key techniques driving AI advancements. By the end, you’ll have a strong grasp of machine learning models and their real-world applications. Whether you're building predictive models, optimizing AI workflows, or advancing in deep learning, this course equips you with practical, job-ready skills.