Build intelligent end-to-end machine learning systems with Python
Key Features
- Use scikit-learn and TensorFlow to train your machine learning models
- Implement popular supervised and unsupervised machine learning algorithms in Python
- Discover best practices for building production-grade machine learning systems from scratch
Book Description
Machine learning enables systems to make predictions based on historical data. Python is one of the most popular languages used to develop machine learning applications, thanks to its extensive library support. This updated third edition of Building Machine Learning Systems with Python helps you get up to speed with the latest trends in artificial intelligence (AI).
With this guide's hands-on approach, you'll learn to build state-of-the-art machine learning models from scratch. Complete with ready-to-implement code and real-world examples, the book starts by introducing the Python ecosystem for machine learning. You'll then learn best practices for preparing data for analysis and later gain insights into implementing supervised and unsupervised machine learning techniques such as classification, regression and clustering. As you progress, you'll understand how to use Python's scikit-learn and TensorFlow libraries to build production-ready and end-to-end machine learning system models, and then fine-tune them for high performance.
By the end of this book, you'll have the skills you need to confidently train and deploy enterprise-grade machine learning models in Python.
What you will learn
- Build a classification system that can be applied to text, images, and sound
- Solve regression-related problems using scikit-learn and TensorFlow
- Recommend products to users based on their previous purchases
- Explore different methods of applying deep neural networks to your data
- Understand recent advances in computer vision and natural language processing (NLP)
- Deploy Amazon Web Services (AWS) to run data models on the cloud
Who this book is for:
This book is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. Prior knowledge of Python programming is expected.
Luis Pedro Coelho is a computational biologist who analyzes DNA from microbial communities to characterize their behavior. He has also worked extensively in bioimage informatics - the application of machine learning techniques for the analysis of images of biological specimens. His main focus is on the processing and integration of large-scale datasets. He has a PhD from Carnegie Mellon University and has authored several scientific publications. In 2004, he began developing in Python and has contributed to several open source libraries. He is currently a faculty member at Fudan University in Shanghai.
Willi Richert has a PhD in machine learning/robotics, where he has used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation. Now at Microsoft, he is involved in various machine learning areas, such as deep learning, active learning, or statistical machine translation. Willi started as a child with BASIC on his Commodore 128. Later, he discovered Turbo Pascal, then Java, then C++-only to finally arrive at his true love: Python.
Matthieu Brucher is a computer scientist who specializes in high-performance computing and computational modeling and currently works for JPMorgan in their quantitative research branch. He is also the lead developer of Audio ToolKit, a library for real-time audio signal processing. He has a PhD in machine learning and signals processing from the University of Strasbourg, two Master of Science degreesone in digital electronics and signal processing and another in automation from the University of Paris XI and Supelec, as well as a Master of Music degree from Bath Spa University.
