Data Science And Machine Learning Reddit
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Just Enough Data Science and Machine Learning
Author: Mark Levene
language: en
Publisher: Addison-Wesley Professional
Release Date: 2024-12-04
An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: Clear explanations of fundamental statistical notions and concepts Coverage of various types of data and techniques for analysis In-depth exploration of popular machine learning tools and methods Insight into specific data science topics, such as social networks and sentiment analysis Practical examples and case studies for real-world application Recommended further reading for deeper exploration of specific topics.
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! 🚀
Machine Learning Algorithms From Scratch with Python
Author: Jason Brownlee
language: en
Publisher: Machine Learning Mastery
Release Date: 2016-11-16
You must understand algorithms to get good at machine learning. The problem is that they are only ever explained using Math. No longer. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work. Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch.