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Building Machine Learning Systems with Python Cover

Building Machine Learning Systems with Python

Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow

Publisher:Packt Publishing Limited
By:  and    
Paid access
|Jul 2018
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Description

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.

Table of Contents

  1. Getting Started with Python Machine Learning
  2. Classifying with Real-world Examples
  3. Regression
  4. Classification I
  5. Dimensionality Reduction
  6. Clustering
  7. Recommendations
  8. Artificial neural Networks & Deep Learning
  9. Classification II
  10. Topic Modeling
  11. Classification III
  12. Computer Vision
  13. Reinforcement Learning
  14. Bigger Data
PDF ISBN: 978-1-78862-222-6
Publisher: Packt Publishing Limited
Copyright owner: © 2018 Packt Publishing
Publication date: 2018
Language: English
Pages: 406
Related subjects:
Computer sciences,
Computer sciences, other

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