Machine Learning for Absolute Beginners. An Introductory Course in Simple Language

A practical and detailed introduction to machine learning.
Simple and clear explanations and the absence of the need for programming experience make this book a great alternative to an academic textbook. It presents the main algorithms of machine learning (ML), accompanied by visual examples and practical work. You will also learn about cross-validation, ensemble modeling, grid search for model tuning, feature design, one-hot encoding, and much more.
To develop intelligent machines, it is essential to understand classical statistics, as algorithms based on it are the heart of machine learning. Writing code is another integral part of ML, which involves data management. However, the material of this guide can be mastered even without programming skills.
This book may be the start of your journey to getting a job in the field of machine learning, or perhaps it will simply satisfy your curiosity.

Inside the guide:
• Download free datasets.
• Methods for data cleansing, including one-hot encoding, clustering, and handling missing data.
• Data preparation for analysis.
• Linear regression analysis.
• Clustering, including k-means clustering.
• Basics of neural networks.
• Bias/variance for improving machine learning models.
• Decision trees for classification decoding.
• Your first machine learning model using Python.

Oliver Theobald is a technical writer specializing in topics of artificial intelligence, financial technology, and cloud computing. He is the author of books such as Python for Absolute Beginners, Machine Learning with Python for Beginners, Data Analytics for Absolute Beginners, and others.