Embark on an in-depth learning journey in machine learning, starting with fundamental concepts like linear regression and logistic regression. You will explore basic supervised learning techniques and the foundations of classification, while also diving into statistical learning. The course structure builds on these foundations, gradually guiding you through cross-validation, bootstrap methods, and model regularization. The second section of the course provides a hands-on approach, with practical labs that cover key algorithms like decision trees, random forests, and support vector machines (SVM). You will also explore deep learning models through neural networks, convolutional neural networks (CNN), and learn about dimensionality reduction techniques such as PCA. Each lab reinforces the theoretical knowledge, helping you develop a clear understanding of each algorithm and its real-world applications. In the final section, the focus shifts to deep learning, with sessions on large language models (LLMs), OpenAI SDK, and LangChain SDK. By the end of the course, you will have acquired the knowledge and skills to implement machine learning models using both traditional algorithms and modern AI technologies. You'll leave with the expertise to apply machine learning concepts to solve complex problems and build powerful models.