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Python Machine Learning By Example

Python Machine Learning By Example

By : Yuxi (Hayden) Liu
5 (2)
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Python Machine Learning By Example

Python Machine Learning By Example

5 (2)
By: Yuxi (Hayden) Liu

Overview of this book

The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML. Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way. With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more. By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.
Table of Contents (15 chapters)
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1
Section 1: Fundamentals of Machine Learning
3
Section 2: Practical Python Machine Learning By Example
12
Section 3: Python Machine Learning Best Practices

Getting Started with Machine Learning and Python

We kick off our Python and machine learning journey with the basic, yet important, concepts of machine learning. We'll start with what machine learning is about, why we need it, and its evolution over a few decades. We'll then discuss typical machine learning tasks and explore several essential techniques of working with data and working with models. It's a great starting point for the subject and we'll learn it in a fun way. Trust me. At the end, we'll also set up the software and tools needed for this book.

We'll go into detail on the following topics:

  • Overview of machine learning and the importance of machine learning
  • The core of machine learning—generalizing with data
  • Overfitting
  • Underfitting
  • Bias variance trade-off
  • Techniques to avoid overfitting
  • Techniques for data preprocessing
  • Techniques...
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