Step-by-step implementations of core data science algorithms from scratch for deep understanding
Updated for Python 3.6 with new material on deep learning, statistics, and natural language processing
Comprehensive crash course in Python, linear algebra, statistics, and probability
Hands-on approach to data collection, cleaning, manipulation, and analysis
Covers machine learning fundamentals including regression, decision trees, neural networks, and clustering
Summarized by Shop
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from sc