Python Machine Learning By Example

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML).

With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements.

At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.

Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP.

By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.

Type
ebook
Category
publication date
2020-10-30
what you will learn

Understand the important concepts in ML 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 analysis 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 2, PyTorch, and scikit-learn

no of pages
526
duration
1052
key features
Dive into machine learning algorithms to solve the complex challenges faced by data scientists today * Explore cutting edge content reflecting deep learning and reinforcement learning developments * Use updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-end
approach
The book has a logical and easy-to-follow structure. It articulates key concepts through both theory and real-world machine learning implementations. Most examples in the book come from the author’s experience working in the field with real data and problems
audience
If you’re a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you.

Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.
meta description
A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques
short description
Equipped with the latest updates, this third edition of Python Machine Learning By Example provides a comprehensive course for ML enthusiasts to strengthen their command of ML concepts, techniques, and algorithms.
subtitle
Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn
keywords
machine learning Python, data visualization, machine learning book, hands on machine learning, natural language processing, Python programming language, Python language
Product ISBN
9781800209718