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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...

Key benefits

Exploit the power of Python to explore the world of data mining and data analytics

Discover machine learning algorithms to solve complex challenges faced by data scientists today

Use Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects

Description

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.

Who is this book for?

If you’re a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.

What you will learn

Understand the important concepts in machine learning and data science

Use Python to explore the world of data mining and analytics

Scale up model training using varied data complexities with Apache Spark

Delve deep into text and NLP using Python libraries such NLTK and gensim

Select and build an ML model and evaluate and optimize its performance

Implement ML algorithms from scratch in Python, TensorFlow, and scikit-learn

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Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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crystalattice Nov 22, 2020

Python ML By Example (BE) is a good complement to Python ML Third Edition (3E). The 3E book focuses on the theory and general application of ML programming, while the BE book focuses an specific application examples.While they both tackle ML programming, their approach is different. The BE book assumes you have a reasonable, foundational background in ML and uses that basis to create specific ML-based applications.For example, whereas 3E has a simple note about Naïve Bayes classification, the BE book has a whole chapter dedicated to the algorithm, discussing the different types of classification methods, how Naïve Bayes works, and then actually implementing a Naïve Bayes application. On the flip side, the 3E book has a whole chapter dedicated just to the different classifiers and different implementations of them using scikit-learn.It's almost like the 3E book is a textbook and the BE book is its complementary workbook for practice. While you may be able to be successful with either one, combining them really maximizes your ML learning.To speak about the BE book in more detail, the topics covered include:*Introduction to Python ML, including software installation*Using Naïve Bayes algorithm to create movie recommendation application*Using SVM for facial recognition*Using tree-based algorithms to predict ad click-through*Using Apache Spark to work with large data sets*Using regression algorithms and neural networks to predict the stock market*Using text analysis and NLP to data mine newsgroups*Using unsupervised learning models to identify newsgroups topics*Using different types of neural networks for different types of analysis approaches*Using reinforcement learning for decision making*ML best practicesIt is a long book (nearly 500 pages), but the material is invaluable for anyone in the ML field, especially if you don't have a lot of experience with the different algorithms. And in conjunction with 3E, you almost have a complete ML curriculum.

Amazon Verified review

Sunil Thapa Mar 03, 2020

Great book to review some machine learning algorithms l.

Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems , Second Edition

What do you get with Print?

Python Machine Learning By Example

Getting Started with Machine Learning and Python

Defining machine learning and why we need it

A very high-level overview of machine learning technology

Core of machine learning – generalizing with data

Preprocessing, exploration, and feature engineering

Combining models

Installing software and setting up

Setting up Python and environments

Summary

Exercises

Page 1 of 9

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