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