Unlock the power of Machine Learning in finance with “Machine Learning for Factor Investing: Python Version.” This essential guide from The Bookish Owl demystifies advanced ML techniques for quantitative finance and algorithmic trading, making them accessible to non-specialists. Explore alpha signal generation, stock selection, and modern investment strategies using firm characteristics. The book offers a deep dive into supervised learning algorithms like tree models and neural networks, alongside complex methods such as autoencoders and causal models, all explained with clear economic rationales. Featuring practical, self-contained Python code samples applied to a large public dataset, this book bridges theory and practice, equipping you with the skills to enhance your financial and technical expertise. Perfect for those with a basic understanding of quantitative finance.