Python Machine Learning By Example, Second Edition

A surging interest in machine learning is due to the fact that it evolutionzies automation by learning patterns in data and using them to make predictions and decisions. Your ML journey starts with this book, as the second edition of the bestseller, Python Machine Learning By Example.

Hayden's unique insights and expertise introduce you to important ML concepts and implementations of algorithms in Python both from scratch and with libraries. Each chapter of the book walks you through an industry adopted application. With the help of realistic examples, you will find it intriguing to acquire mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP - they are no more obscure as you thought.

This critically extended and updated edition now includes implementation with trendy libraries including TensorFlow, gensim and Keras. The scikit-learn codes are also fully modernized. Even if you've read the last edition, you'll still be delighted to find plenty of new content, for example, neural network, dimensionality reduction, topic modeling, large-scale learning with Spark and word embedding.

Toward the end, you will gather a broad picture of the ML ecosystem and best practices of applying ML techniques to meet new opportunities in today's world.

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

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.