The course begins with a gentle introduction to machine learning, providing context for the subsequent chapters. As it progresses, readers are gradually introduced to key supervised learning algorithms, such as linear regression, decision trees, support vector machines, and neural networks. Each algorithm is explained in a step-by-step manner, including practical examples and real-world use cases, ensuring that readers grasp the material with clarity.
This book offers a comprehensive guide to unsupervised machine learning and feature engineering concepts. Spanning six units, the course starts by laying a solid foundation in unsupervised learning, covering essential concepts such as clustering, dimensionality reduction, and anomaly detection. It then delves into feature engineering, a critical aspect of machine learning that significantly impacts model performance.
This book offers an overview of Neural Nets and Deep Learning. I gained a comprehensive understanding of core concepts, explored neural network architectures in depth, and acquired hands-on experience with convolutional and recurrent networks. I can now optimize networks for peak performance and delve into the "why" behind their operation, empowering me to create intelligent systems independently.