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This book Python Machine Learning Blueprints - Second Edition will help you discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras.

By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects.

What You Will Learn
- Understand the Python data science stack and commonly used algorithms
- Build a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window
- Understand NLP concepts by creating a custom news feed
- Create applications that will recommend GitHub repositories based on ones you’ve starred, watched, or forked
- Gain the skills to build a chatbot from scratch using PySpark
- Develop a market-prediction app using stock data
- Delve into advanced concepts such as computer vision, neural networks, and deep learning


Authors
Alexander Combs
Alexander Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He currently lives and works in New York City.

Michael Roman
Michael Roman is a data scientist at The Atlantic, where he designs, tests, analyzes, and productionizes machine learning models to address a range of business topics. Prior to this he was an associate instructor at a full-time data science immersive program in New York City. His interests include computer vision, propensity modeling, natural language processing, and entrepreneurship.