Machine Learning Essentials For Everybody

***** BUY NOW (will soon return to 21.97 $) *****Are you thinking of mastering machine learning fundamentals?If you are looking for a beginner book to master machine learning fundamentals, this book is for you.The book presents a theoretical overview of the underlying principles on which the entire machine learning stack is based. This includes sections about statistics, probability and machine learning.Regardless of the level of expertise of the reader, be it a beginner or a seasoned professional, there is lots of distilled knowledge available in these pages, which would give the reader a new perspective on what machine learning is all about. From AI Sciences PublishingOur books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. Readers are advised to adopt a hands on approach, which would lead to better mental representations. Who Should Read This?This book presents the foundational principles guiding machine learning field. It also present many examples and illustrations. The following groups of people would benefit maximally from from this book: The reader who has heard about the impact data science is set to make across industries but isn't quite sure what skills are required to get a footing in the field. This set of readers can expect to profit from the clear explanations of basic concepts and build intuitions that enable them to transition on to more complex topics. The practitioner who has intermediate level skills in the related fields of statistics, mathematics, and computer science but wants to understand in what ways machine learning is a different discipline. This type of reader would understand the concepts presented in this book quickly as machine learning is an interdisciplinary field that sits at the intersection between many well established scientific fields. The practicing data scientist or experienced veteran would appreciate this book for providing a refresher on many common concepts and a whirlwind tour of what is currently obtainable in terms of best practices. The breadth of this book is such that this reader would have a reference manual of sorts for how to master main machine learning techniques. What's Inside This Book? Artificial intelligence, Machine learning and their applications Laying the Foundation What is Machine Learning? Why Machine Learning? The Math behind Machine Learning for Beginners: Linear Algebra and Statistics Probability, conditional Probability and Distributions Link between Statistics and machine learning Supervised Learning Unsupervised Learning Semi-supervised Learning Reinforcement Learning Summarizing the Dataset Data Visualization Linear Regression Logistic Regression Decision Tree and Forest Algorithm SVM (Support Vector Machines) Naïve Bayes Algorithm Clustering KNN (K-Nearest Neighbors) Neural Networks for beginner Frequently Asked QuestionsQ: 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 all aspects. Q: Can I have a refund if this book doesn't fit for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform.***** MONEY BACK GUARANTEE BY AMAZON *****

The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Unleash the power of machine learning to automate tasks, make predictions, and solve complex problems. Machine learning is a powerful tool that can be used to automate tasks, make predictions, and solve complex problems. It is used in a wide variety of industries, including healthcare, finance, and manufacturing. Machine Learning Made Easy is the perfect resource for anyone who wants to learn the basics of machine learning. This comprehensive guide covers everything you need to know, from the basics of machine learning algorithms to advanced topics such as deep learning. Whether you're a student, a business professional, or a data enthusiast, Machine Learning Made Easy is the essential resource for learning about machine learning. Here are some of the key topics covered in the book: Introduction to machine learning Types of machine learning algorithms Choosing the right machine learning algorithm Training a machine learning model Evaluating a machine learning model Using machine learning to automate tasks Using machine learning to make predictions If you are a beginner who wants to learn about machine learning, Machine Learning Made Easy is a great place to start.

Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models. The main parts of the book include: A) Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. B) Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. C) Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. D) Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). E) Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. F) Model validation and evaluation techniques for measuring the performance of a predictive model. G) Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: - Covers machine learning algorithm and implementation - Key mathematical concepts are presented - Short, self-contained chapters with practical examples.

★★ Buy the Paperback Version of this Book and get the Kindle Book Version for FREE ★★ Do you need a better knowledge of the possibilities existing in the artificial intelligence available today? Do you want to know how big data will shape the future? Do you want to achieve a professional understanding of the most commonly used machine learning models? Machine learning is a branch of artificial intelligence and computer science that's becoming increasingly relevant in our modern world. It's a relatively new and progressive way of allowing a computer model to improve over time as it is introduced to more data. With the widespread availability of computers today, most machine learning techniques can be done at home. From the GPS on our phones to the future of self-driving cars, machine learning is becoming more relevant to our lives every day. Every time our email inbox sorts spam email there is a machine learning model involved. When we use voice recognition on our phone neural networks are sorting and analyzing our words. This book will give you the key terms and basic understanding of the fastest-growing field in computer science as well as: A breakdown of machine learning techniques and algorithms; why, and how they are used The tools you will need. Where to find data, what languages work best for machine learning, and what technology is available to help you Practical examples of machine learning being used in the modern world The basic statistics and mathematics necessary to understand and interpret data A jumping off point to begin diving into this fascinating technology Even if you aren't an expert in mathematics or computer programming, you will learn the basics of machine learning from this book. if you are ready to know how machine learning models work, check out now this guidebook to help you get started. Scroll Up and Click the "Buy now" button!

"Python Machine Learning Essentials" by Bernard Baah is your ultimate guide to mastering machine learning concepts and techniques using Python. Whether you're a beginner or an experienced programmer, this book equips you with the knowledge and skills needed to understand and apply machine learning algorithms effectively. With a comprehensive approach, Bernard Baah takes you through the fundamentals of machine learning, covering Python basics, data preprocessing, exploratory data analysis, supervised and unsupervised learning, neural networks, natural language processing, model deployment, and more. Each chapter is filled with practical examples, code snippets, and hands-on exercises to reinforce your learning and deepen your understanding. As the founder of Filly Coder (https: //fillycoder.com), Bernard Baah brings years of experience in machine learning and software development to this book. His expertise and passion for teaching shine through, making complex concepts accessible and understandable for readers of all levels. Whether you're a data scientist, developer, or aspiring AI enthusiast, "Python Machine Learning Essentials" is your go-to resource for mastering machine learning with Python. Dive into the world of machine learning and unlock the potential to build intelligent applications with confidence. Get your copy of "Python Machine Learning Essentials" today and embark on your journey to becoming a proficient machine learning practitioner

Data visualization is one of the most important part of data science. Many books and courses present a catalogue of graphics but they don't teach you which charts to use according to the type of the data. In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems. Next, we provide more than 200 practical examples to create great graphics for the right data using either the ggplot2 package and extensions or the traditional R graphics. With this book, you 'll learn: - How to quickly create beautiful graphics using ggplot2 packages - How to properly customize and annotate the plots - Type of graphics for visualizing categorical and continuous variables - How to add automatically p-values to box plots, bar plots and alternatives - How to add marginal density plots and correlation coefficients to scatter plots - Key methods for analyzing and visualizing multivariate data - R functions and packages for plotting time series data - How to combine multiple plots on one page to create production-quality figures.

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

Machine learning is a powerful tool for making accurate predictions and improving decision-making based on data-driven insights. However, building accurate and reliable machine learning models requires a thorough understanding of the machine learning workflow, from data preprocessing and exploration to model training and deployment. In this ebook, we provide a practical guide to machine learning essentials, covering everything from the basics of supervised and unsupervised learning to deep learning and model deployment. We explore common machine learning algorithms, including decision trees, support vector machines, and neural networks, and provide examples of how they can be used in real-world applications. We also delve into data preprocessing and exploration, discussing techniques for cleaning, transforming, and scaling data to make it suitable for analysis, and exploring ways to gain insights into the properties and relationships of the data. We discuss best practices for model training and evaluation, and explore strategies for deploying and maintaining machine learning models in a production environment. Whether you're an experienced data scientist or just starting out, this ebook provides a comprehensive guide to building accurate and reliable machine learning models that can transform your business and improve decision-making based on data-driven insights.

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.