Machine Learning for Absolute Beginners: A Plain English Introduction (Third Edition) (Machine Learning with Python for Beginners Book Series) (2021 Edition)

By Oliver Theobald

The book “Machine Learning for Absolute Beginners: A Plain English Introduction (Third Edition)” is designed to provide a comprehensive yet accessible introduction to the field of machine learning, particularly for those who are new to the subject. This edition is part of the “Machine Learning with Python for Beginners Book Series” 

The book is aimed at beginners with little to no prior knowledge of machine learning or Python programming. It’s designed to be a stepping stone for those interested in entering the field, whether for academic, professional, or personal reasons.

Key Concepts:

  • Foundational Principles: The book starts by laying down the foundational principles of machine learning, explaining what it is, why it’s important, and where it is applied.
  • Python Programming: Given that it’s part of a series focused on Python, the book likely includes practical coding exercises and examples to help you understand machine learning algorithms using Python.
  • Algorithms and Models: The book covers various machine learning algorithms and models, explaining how they work and when to use them. This could range from supervised to unsupervised learning models.
  • Data Handling: One of the crucial aspects of machine learning is data manipulation and analysis. The book is expected to cover how to handle data sets, clean data, and prepare it for machine learning tasks.
  • Practical Applications: The book likely includes real-world examples and case studies to demonstrate the applications of machine learning in various industries.
  • Ethical Considerations: Given the comprehensive nature of the book, it may also touch upon the ethical considerations in machine learning, such as data privacy and algorithmic bias.

The book covers topics like

• How to download free datasets

• What tools and machine learning libraries you need

• Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data

• Preparing data for analysis, including k-fold Validation

• Regression analysis to create trend lines

• k-Means Clustering to find new relationships

• The basics of Neural Networks

• Bias/Variance to improve your machine learning mode

• Decision Trees to decode classification, and

• How to build your first Machine Learning Model to predict house values using Python

While The book will not make you an expert in artificial intelligence or Python, it is a good starting point for the novices.

Available at