Summary
In this comprehensive chapter, you journeyed through the fundamental concepts and practical applications of ML. You began by understanding the core principles of ML and its close relationship with data science, emphasizing the pivotal role of data in training and evaluating ML models. You explored different types of ML, ranging from supervised and unsupervised learning to reinforcement learning and deep learning. Each type was elucidated with real-world examples and common algorithms, providing you with an understanding of when and how to apply them.
Next, you delved into the critical concepts of model overfitting and underfitting, exploring the delicate balance required to achieve model generalization. You examined various strategies and techniques to address these challenges effectively.
Popular AI tools and frameworks were covered and the chapter also ventured into cloud-based ML, demonstrating the advantages and capabilities of harnessing cloud platforms for ML...
