Grokking Machine Learning Book Pdf
Download Grokking Machine Learning Book Pdf PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Grokking Machine Learning Book Pdf book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Grokking Machine Learning
Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.
Mastering Machine Learning: A Friendly Guide to Understanding How AI Learns
If you've ever wondered how Netflix always knows what you want to watch… If you've felt overwhelmed by the buzz around artificial intelligence but wished someone would just explain it simply… If you're a student, professional, or curious mind looking to use AI without needing a tech degree… This book is for you. Demystifying the Smart Tech Behind Chatbots, Face Recognition, and Predictive Magic—For Curious Minds of All Ages Mastering Machine Learning: A Friendly Guide to Understanding How AI Learns is your god-sent crash course into the invisible power behind the tech we use every day. It’s not just a book—it’s your personal guide to unlocking smart solutions for everyday problems. Packed with: ✅ Tips & Tricks anyone can use, with step-by-step guides for building your own smart tools ✅ Real-life stories of how machine learning has transformed homes, classrooms, and businesses ✅ Eye-popping illustrations & relatable analogies that make complex ideas surprisingly easy ✅ DIY projects & cheat sheets for hands-on learning—even if you’re tech-shy ✅ Ethical insights to help you use AI responsibly and wisely ✅ Bonus content on how sci-fi inspired today’s smart tech Whether you're a curious teen, a creative entrepreneur, or a life-long learner, this book is your backstage pass into the world of learning machines—and how they can help you learn, grow, and thrive. GET YOUR COPY TODAY! 🚀
Applied Machine Learning for Data Science Practitioners
Author: Vidya Subramanian
language: en
Publisher: John Wiley & Sons
Release Date: 2025-04-01
A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the foundation for understanding ML. Data Preparation covers the process of framing ML problems and preparing data and features for modeling. ML Problem Solving introduces you to a range of ML algorithms, including Regression, Classification, Ranking, Clustering, Patterns, Time Series, and Anomaly Detection. Model Optimization explores frameworks, decision trees, and ensemble methods to enhance performance and guide the selection of the most effective model. ML Ethics addresses ethical considerations, including fairness, accountability, transparency, and ethics. Model Deployment and Monitoring focuses on production deployment, performance monitoring, and adapting to model drift.