<h2>Make better decisions with this easy deconstructed guide to data analytics.</h2> <h3>Want to add data analytics to your skill stack? Having trouble finding where to start?</h3> Cell by cell, bit by bit, this book teaches you the vocabulary, tools, and basic algorithms to think like a data scientist. Like assembling a complex Lego set, each chapter connects and adds individual blocks of knowledge to build your data literacy. This linear structure to unpacking data analytics takes you from zero to confidently analyzing and discussing data problems like a data scientist. <h3>Who is This Book For?</h3> This book is ideal for anyone interested in making sense of data analytics without the assumption that you understand data science terminology or advanced math. If you've tried to learn data analytics before and failed, this book is for you. <h3>A Practical Approach</h3> This book takes a hands-on approach to learning. This includes practical examples, visual examples, as well as two bonus coding exercises in Python, including free video content to walk you through both exercises. By the end of the book, you will have the practical knowledge to tackle real data problems in your organization or daily life. <h3>What You Will Learn From Reading This Book</h3> * How to recognize the common data types every data scientist needs to master * Where to store your data, including Big Data * New trends in data analytics, including what is Alternative Data and why not many people know about it * How to explain the distinction between Data Mining, Machine Learning, and Analytics to your colleagues * When and how to use Regression Analysis, Classification, Clustering, Association Analysis, and Natural Language Processing * How to make better business decisions using Data Visualization and Business Intelligence

Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy: (Introduction to Data, Data Visualization, Business Intelligence & ... Data Science & Python Books for Beginners)

Science & Math

Theobald, Oliver

Mathematics

Independently published

Please note the second edition of this title with updated content is now available on Amazon. Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile? Well, hold on there... Before you embark on your epic journey into the world of machine learning, there is basic theory to march through first. But 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 offers a practical and high-level introduction to machine learning. Machine Learning for Absolute Beginners 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. This title opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into introducing specific algorithms applied in machine learning, including their pros and cons. At the end of the book, I share insights and advice on further learning and careers in this space. Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle deep learning and Scikit-learn, 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. In this step-by-step guide you will learn:- The very basics of Machine Learning that all beginners need to master - Association Analysis used in the retail and E-commerce space - Recommender Systems as you've seen online, including Amazon - Decision Trees for visually mapping and classifying decision processes - Regression Analysis to create trend lines and predict trends - Data Reduction and Principle Component Analysis to cut through the noise - k-means and k-nearest Neighbor (k-nn) Clustering to discover new data groupings - Introduction to Deep Learning/Neural Networks - Bias/Variance to optimize your machine learning model - How to build your first machine learning model to predict video game sales using Python - Careers in the field Please also note that under Amazon’s Matchbook program, the purchaser of this book can add the Kindle version of this title (valued at $3.99 USD) to their Amazon Kindle library at no cost.

Machine Learning for Absolute Beginners: A Plain English Introduction

Computers & Technology

Computer Science

Featured by Tableau as the first of "7 Books About Machine Learning for Beginners" Ready to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?Well, hold on there...Before you embark on your epic journey into the world of machine learning, there is some theory and statistical principles to march through first. But 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. Machine 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.This 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. Disclaimer: 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.In this step-by-step guide you will learn:- How to download free datasets- What tools and machine learning libraries you need- Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data- Preparing data for analysis, including k-fold Validation- Regression analysis to create trend lines- Clustering, including k-means and k-nearest Neighbors- The basics of Neural Networks- Bias/Variance to improve your machine learning model- Decision Trees to decode classification- How to build your first Machine Learning Model to predict house values using PythonFrequently Asked QuestionsQ: Do I need programming experience to complete this book?A: This book is designed for absolute beginners, so no programming experience is required. However, two of the later chapters introduce Python to demonstrate an actual machine learning model, so you will see programming language used in this book. Q: I have already purchased the First Edition of this book, should I purchase this Second Edition?A: As majority of the topics from the First Edition are covered in the Second Edition, you may be better served reading a more advanced title on machine learning.Q: Can I get access to the Kindle version of this book?A: Yes. Under Amazon’s Matchbook program, the purchaser of this book can add the Kindle version of this title (valued at $3.99 USD) to their Amazon Kindle library at no cost.Q: Does this book include everything I need to become a machine learning expert?A: This book is designed for readers taking their first steps in machine learning and further learning will be required beyond this book to master machine learning.

Machine Learning For Absolute Beginners: A Plain English Introduction (Learn AI, Data Science & Python Books for Beginners)

As the second title in the Machine Learning for Beginners series, this book teaches beginners to code basic machine learning models using Python. The book is designed for beginners with basic background knowledge of machine learning, including common algorithms such as logistic regression and decision trees. If this doesn't describe your experience or if you need a refresher, key concepts from machine learning in the opening chapter and there are overviews of specific algorithms dispersed throughout this book. For a gentle and more detailed explanation of machine learning theory minus the code, I suggest reading the first book in this series Machine Learning for Absolute Beginners (Second Edition), which is written for a more general audience. <h3>In this step-by-step guide you will learn:</h3> - To code practical machine learning prediction models using a range of supervised learning algorithms including logistic regression, gradient boosting, and decision trees- Clean and inspect your data using free machine learning libraries- Visualize relationships in your dataset including Heatmaps and Pairplots using just a few lines of simple code- Develop your expertise in managing data using Python

Machine Learning with Python: A Practical Beginners’ Guide (Learn Machine Learning with Python Books for Beginners)

<h2>Learn How to Make Your Own Recommender System in an Afternoon.</h2> Recommender systems are one of the most visible applications of machine learning and their uncanny ability to convert our unspoken actions into items we like is both addicting and concerning. Recommender systems, though, are here to stay and for anyone beginning their journey in data science, this is a lucrative space for future employment. This book will get you up and running with the basics as well as the steps to coding your own recommender system using Python. Exercises include predicting book recommendations, relevant house properties for online marketing purposes, and whether a user will click on an ad campaign. <h3>Who is the Book For?</h3> The contents of this book is designed for beginners with some background knowledge of data science, including classical statistics and computing programming. If this is your first exposure to data science, you may want to spend a few hours to read my first book Machine Learning for Absolute Beginners before you get started here. <h3>Topics covered in this book:</h3> * How to set up a free and easy sandbox environment using Jupyter Notebook * How to prepare your data for processing * How to code a collaborative filtering model * How to code a content-based filtering model * How recommender systems are evaluated * What you need to know about privacy and ethics * What the future of Recommender Systems might look like

Machine Learning: Make Your Own Recommender System (Learn Machine Learning with Python Books for Beginners)

Chatgpt Prompts Book: Precision Prompts, Priming, Training & Ai Writing Techniques For Mortals

Packt Publishing Ltd.

AI for Absolute Beginners: A Clear Guide to Tomorrow (Learn AI, Data Science & Python Books for Beginners)

Scatterplot Press

Featured by Tableau as the first of "7 Books About Machine Learning for Beginners."\nReady to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?\nWell, hold on there...\nBefore you embark on your journey, there are some high-level theory and statistical principles to weave through first. 
But rather than spend $30-$50 USD on a thick textbook, you may want to read this book first. As a clear and concise alternative, this book provides a high-level introduction to machine learning, free downloadable code exercises, and video demonstrations.\nMachine Learning for Absolute Beginners Third 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 to follow along at home.\nNew Updated Edition 
This new edition features extended chapters with quizzes, free supplementary online video tutorials for coding models in Python, and downloadable resources not included in the Second Edition.\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 give a clear lay of the land.\nIn This Step-By-Step Guide You Will Learn: 
- How to download free datasets 
- What tools and machine learning libraries you need 
- Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data 
- Preparing data for analysis, including k-fold Validation 
- Regression analysis to create trend lines 
- k-Means Clustering to find new relationships 
- The basics of Neural Networks 
- Bias/Variance to improve your machine learning model 
- Decision Trees to decode classification, and 
- How to build your first Machine Learning Model to predict house values using Python\nFrequently Asked Questions 
Q: Do I need programming experience to complete this e-book? A: This e-book is designed for absolute beginners, so no programming experience is required. However, two of the later chapters introduce Python to demonstrate an actual machine learning model, so you will see some programming used in this book.\nQ: I have already purchased the Second Edition of Machine Learning for Absolute Beginners, should I purchase this Third Edition? 
A: As the same topics from the Second Edition are covered in the Third Edition, you may be better served reading a more advanced title on machine learning. If you have purchased a previous edition of this book and wish to get access to the free video tutorials, please email the author.\nQ: Does this book include everything I need to become a machine learning expert? 
A: Unfortunately, no. This book is designed for readers taking their first steps in machine learning and further learning will be required beyond this book to master machine learning.

Machine Learning for Absolute Beginners: A Plain English Introduction (Third Edition)

ChatGPT Prompts Book: Precision Prompts, Priming, Training & AI Writing Techniques for Mortals (2025 Learn AI Text Prompt Engineering Series)

Reference

Writing, Research & Publishing Guides

Generative AI Art: A Beginner’s Guide to 10x Your Output with Smart Text Prompts (Midjourney, DALL-E, Craiyon) (2025 Learn AI Text Prompt Engineering Series)

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