Machine Learning For Absolute Beginners: A Plain English Introduction - Oliver Theobald

\nFeatured by Tableau as the first of 7 Books About Machine Learning for Beginners \n \nReady to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile? \n \nWell, hold on there... \n \nBefore you embark on your epic journey into the world of machine learning, there is some theory and statistical principles to march through first. \n \nBut rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this book provides a practical and high-level introduction to the practical components and statistical concepts found in machine learning. \n \nMachine Learning for Absolute Beginners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home. \n \nThis major new edition features many topics not covered in the First Edition, including Cross Validation, Data Scrubbing and Ensemble Modeling. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Readers of the First Edition should not feel compelled to purchase this Second Edition. \n \nDisclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment-as a fully grown Simba looking over the Pride Lands of Africa-then this is the book to gently hoist you up and offer you a clear lay of the land. \n \nIn this step-by-step guide you will learn: \n- How to download free datasets \n- What tools and machine learning libraries you need \n- Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data \n- Preparing data for analysis, including k-fold Validation \n- Regression analysis to create trend lines \n- Clustering, including k-means and k-nearest Neighbors \n- The basics of Neural Networks \n- Bias/Variance to improve your machine learning model \n- Decision Trees to decode classification \n- How to build your first Machine Learning Model to predict house values using Python \n \nFrequently Asked Questions \nQ: Do I need progra