Download or read book The Hitchhiker s Guide to Machine Learning Algorithms written by Devin Schumacher and published by SERP Media. This book was released on 2023-07-26 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hello humans & welcome to the world of machines! Specifically, machine learning & algorithms. We are about to embark on an exciting adventure through the vast and varied landscape of algorithms that power the cutting-edge field of artificial intelligence. Machine learning is changing the world as we know it. From predicting stock market trends and diagnosing diseases to powering the virtual assistants in our smartphones and enabling self-driving cars, and picking up the slack on your online dating conversations. What makes this book unique is its structure and depth. With 100 chapters, each dedicated to a different machine learning concept, this book is designed to be your ultimate guide to the world of machine learning algorithms. Whether you are a student, a data science professional, or someone curious about machine learning, this book aims to provide a comprehensive overview that is both accessible and in-depth. The algorithms covered in this book span various categories including: Classification & Regression: Learn about algorithms like Decision Trees, Random Forests, Support Vector Machines, and Logistic Regression which are used to classify data or predict numerical values. Clustering: Discover algorithms like k-Means, Hierarchical Clustering, and DBSCAN that group data points together based on similarities. Neural Networks & Deep Learning: Dive into algorithms and architectures like Perceptrons, Convolutional Neural Networks (CNN), and Long Short-Term Memory Networks (LSTM). Optimization: Understand algorithms like Gradient Descent, Genetic Algorithms, and Particle Swarm Optimization which find the best possible solutions in different scenarios. Ensemble Methods: Explore algorithms like AdaBoost, Gradient Boosting, and Random Forests which combine the predictions of multiple models for improved accuracy. Dimensionality Reduction: Learn about algorithms like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) which reduce the number of features in a dataset while retaining important information. Reinforcement Learning: Get to know algorithms like Q-learning, Deep Q-Network (DQN), and Monte Carlo Tree Search which are used in systems that learn from their environment. Each chapter is designed as a standalone introduction to its respective algorithm. This means you can start from any chapter that catches your interest or proceed sequentially. Along with the theory, practical examples, applications, and insights into how these algorithms work under the hood are provided. This book is not just an academic endeavor but a bridge that connects theory with practical real-world applications. It's an invitation to explore, learn, and harness the power of algorithms to solve complex problems and make informed decisions. Fasten your seat belts as we dive into the mesmerizing world of machine learning algorithms. Whether you are looking to expand your knowledge, seeking inspiration, or in pursuit of technical mastery, this book should sit on your coffee table and make you look intelligent in front of all invited (and uninvited) guests.
Download or read book The Hitchhiker s Guide to AI written by Arthur Goldstuck and published by Pan Macmillan South africa. This book was released on 2023-12-01 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the past decade, Arthur Goldstuck has had a front-row seat to witness the remarkable rise of AI across all sectors of business and society. As generative AI becomes a household phrase and sparks hopes and fears of machines augmenting or replacing human beings, this guide offers an invaluable overview of the past, present and future of AI. The Hitchhiker’s Guide to AI is aimed at both beginners and those who consider themselves experienced or skilled at using AI. It draws on many years of direct access to global and regional leaders in using AI, from Africa to the Middle East to North America to Europe and Asia, and it provides unique perspectives on generative AI, as well as practical advice for using it. It is useful for consumers, academics, professionals and anyone in business who wants to get up to speed quickly and practically. It also entertains and inspires anyone who is curious about AI or already engaged in its possibilities. Need to understand or refine prompting? You’re in the right place. Need to prepare for the coming impact of AI on health, travel, education and business? This is the book for you.
Download or read book The Hitchhiker s Guide to Python written by Kenneth Reitz and published by "O'Reilly Media, Inc.". This book was released on 2016-08-30 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Hitchhiker's Guide to Python takes the journeyman Pythonista to true expertise. More than any other language, Python was created with the philosophy of simplicity and parsimony. Now 25 years old, Python has become the primary or secondary language (after SQL) for many business users. With popularity comes diversityâ??and possibly dilution. This guide, collaboratively written by over a hundred members of the Python community, describes best practices currently used by package and application developers. Unlike other books for this audience, The Hitchhikerâ??s Guide is light on reusable code and heavier on design philosophy, directing the reader to excellent sources that already exist.
Download or read book Artificial Intelligence and Soft Computing written by Leszek Rutkowski and published by Springer Nature. This book was released on 2021-10-05 with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 12854 and 12855 constitutes the refereed proceedings of the 20th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2021, held in Zakopane, Poland, in June 2021. Due to COVID 19, the conference was held virtually. The 89 full papers presented were carefully reviewed and selected from 195 submissions. The papers included both traditional artificial intelligence methods and soft computing techniques as well as follows: · Neural Networks and Their Applications · Fuzzy Systems and Their Applications · Evolutionary Algorithms and Their Applications · Artificial Intelligence in Modeling and Simulation · Computer Vision, Image and Speech Analysis · Data Mining · Various Problems of Artificial Intelligence · Bioinformatics, Biometrics and Medical Applications
Download or read book The Hitchhiker s Guide to ChatGPT written by Devin Schumacher and published by SERP. This book was released on 2023-02-25 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Everything You Need to Know to Get Started Using ChatGPT to VASTLY Improve Your Efficiency, Profit & ROI in Your Business Or Start a New Business, Create Additional Revenue Streams & Fully Leverage Artificial Intelligence to Make Your Life Better Artificial Intelligence is HERE! ... And there's not much "artificial" about it. Used Correctly, AI Will Help You: Produce More: With AI, you can automate repetitive and time-consuming tasks, freeing up time to focus on creative and fulfilling work. This can enhance your personal happiness and fulfillment, while increasing productivity and efficiency in the workplace. Earn More: AI can increase productivity and efficiency, leading to greater income potential. You can also use AI-powered financial management tools to better manage your finances and make better investment decisions, potentially leading to greater wealth accumulation and financial freedom. Live More: Automate menial and trivial tasks, you can create more balance in your life and spend more time on what matters most, such as family, hobbies, and personal development. This can increase overall happiness and fulfillment. Enjoy More: More time means more pursuit of your passions & interests, ultimately leading to greater fulfillment and happiness. ... And that just scratches the surface. IN THIS BOOK: Intro to AI & ChatGPT: Get ahead of the competition and transform your business with the power of AI, even if you have no prior experience or technical expertise. Unlock unlimited possibilities for your business, driving growth and success like never before. Real-life Examples for Every industry: Discover the many ways that AI can revolutionize your industry, from improving healthcare outcomes and streamlining finance to enhancing customer service and increasing productivity. Prompts: Harness the true power of AI by understanding the importance of prompts, which can help you achieve extraordinary results, from personalized customer experiences to more accurate data analysis. Prompt Engineering: Become a master of prompt engineering and create tailored prompts that make your AI-powered applications more effective and efficient - No prior engineering experience needed. Prompt Examples: Plug & play prompts you can use these examples to inspire your own winning prompts, creating a competitive edge that sets your business apart from the rest. Jailbreaking: Unleash the full potential of AI by breaking free from traditional limitations and creating innovative solutions that push the boundaries of what's possible in your industry. Tools, Extensions & Enhancements: Access a wealth of tools, extensions, and add-ons that can help you maximize the benefits of AI and ChatGPT, driving greater results and generating more revenue - no technical knowledge required. Access to a community of experts: Get support and guidance from a community of AI and ChatGPT experts, helping you overcome challenges and make the most of this transformative technology - practically instantly. Ensure your business is always on the cutting edge of AI innovation. Hundreds of business use cases & ideas for making money with ChatGPT: Tap into an endless supply of ideas and strategies for using ChatGPT to grow your business and generate more revenue, from optimizing workflows and increasing productivity to creating personalized customer experiences and increasing sales.
Download or read book Machine Learning Algorithms From Scratch with Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2016-11-16 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Machine Learning Algorithms written by Giuseppe Bonaccorso and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 799 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key FeaturesUpdated to include new algorithms and techniquesCode updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applicationsBook Description Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios. What you will learnUnderstand the characteristics of a machine learning algorithmImplement algorithms from supervised, semi-supervised, unsupervised, and RL domainsLearn how regression works in time-series analysis and risk predictionCreate, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANsWho this book is for This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.
Download or read book Machine Learning for Beginners written by Steven Cooper and published by Roland Bind. This book was released on 2018-09-07 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are looking for a complete beginners guide to learn machine learning with examples, in just a few hours, then you need to continue reading. Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that. ★★ Grab your copy today and learn ★★ ♦ The different types of learning algorithm that you can expect to encounter ♦ The numerous applications of machine learning ♦ The different types of machine learning and how they differ ♦ The best practices for picking up machine learning ♦ What languages and libraries to work with ♦ The future of machine learning ♦ The various problems that you can solve with machine learning algorithms ♦ And much more... Starting from nothing, we slowly work our way through all the concepts that are central to machine learning. By the end of this book, you're going to feel as though you have an extremely firm understanding of what machine learning is, how it can be used, and most importantly, how it can change the world. You're also going to have an understanding of the logic behind the algorithms and what they aim to accomplish. Don't waste your time working with a book that's only going to make an already complicated topic even more complicated. Scroll up and click the buy now button to learn everything you need to know about Machine Learning!
Download or read book Machine Learning For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2021-02-09 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more. Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learning Work with Python 3.8 and TensorFlow 2.x (and R as a download) Build and test your own models Use the latest datasets, rather than the worn out data found in other books Apply machine learning to real problems Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.
Download or read book Machine Learning Made Easy A Beginner s Guide for All written by M.B. Chatfield and published by M.B. Chatfield. This book was released on with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Methods and Theory in the Organizational Sciences written by Kevin R. Murphy and published by Taylor & Francis. This book was released on 2022-02-20 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data, Methods and Theory in the Organizational Sciences explores the long-term evolution and changing relationships between data, methods, and theory in the organizational sciences. In the last 50 years, theory has come to dominate research and scholarship in these fields, yet the emergence of big data, as well as the increasing use of archival data sets and meta-analytic methods to test empirical hypotheses, has upset this order. This volume examines the evolving relationship between data, methods, and theory and suggests new ways of thinking about the role of each in the development and presentation of research in organizations. This volume utilizes the latest thinking from experts in a wide range of fields on the topics of data, methods, and theory and uses this knowledge to explore the ways in which behavior in organizations has been studied. This volume also argues that the current focus on theory is both unhealthy for the field and unsustainable, and it provides more successful ways theory can be used to support and structure research, and demonstrates the most effective techniques for analyzing and making sense of data. This is an essential resource for researchers, professionals, and educators who are looking to rethink their current approaches to research, and who are interested in creating more useful and more interpretable research in the organizational sciences.
Download or read book Controlling Privacy and the Use of Data Assets Volume 1 written by Ulf Mattsson and published by CRC Press. This book was released on 2022-06-27 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Ulf Mattsson leverages his decades of experience as a CTO and security expert to show how companies can achieve data compliance without sacrificing operability." Jim Ambrosini, CISSP, CRISC, Cybersecurity Consultant and Virtual CISO "Ulf Mattsson lays out not just the rationale for accountable data governance, he provides clear strategies and tactics that every business leader should know and put into practice. As individuals, citizens and employees, we should all take heart that following his sound thinking can provide us all with a better future." Richard Purcell, CEO Corporate Privacy Group and former Microsoft Chief Privacy Officer Many security experts excel at working with traditional technologies but fall apart in utilizing newer data privacy techniques to balance compliance requirements and the business utility of data. This book will help readers grow out of a siloed mentality and into an enterprise risk management approach to regulatory compliance and technical roles, including technical data privacy and security issues. The book uses practical lessons learned in applying real-life concepts and tools to help security leaders and their teams craft and implement strategies. These projects deal with a variety of use cases and data types. A common goal is to find the right balance between compliance, privacy requirements, and the business utility of data. This book reviews how new and old privacy-preserving techniques can provide practical protection for data in transit, use, and rest. It positions techniques like pseudonymization, anonymization, tokenization, homomorphic encryption, dynamic masking, and more. Topics include Trends and Evolution Best Practices, Roadmap, and Vision Zero Trust Architecture Applications, Privacy by Design, and APIs Machine Learning and Analytics Secure Multiparty Computing Blockchain and Data Lineage Hybrid Cloud, CASB, and SASE HSM, TPM, and Trusted Execution Environments Internet of Things Quantum Computing And much more!
Download or read book Calculus for Machine Learning written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2022-02-23 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Calculus seems to be obscure, but it is everywhere. In machine learning, while we rarely write code on differentiation or integration, the algorithms we use have theoretical roots in calculus. If you ever wondered how to understand the calculus part when you listen to people explaining the theory behind a machine learning algorithm, this new Ebook, in the friendly Machine Learning Mastery style that you’re used to, is all you need. Using clear explanations and step-by-step tutorial lessons, you will understand the concept of calculus, how it is relates to machine learning, what it can help us on, and much more.
Download or read book Machine Learning for Beginners written by William J Ford and published by . This book was released on 2020-02-18 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: You Are About To Start Your Journey To Understanding Machine Learning Like The Back Of Your Hand And Use It To Your Advantage! If you've always wanted to learn how computers are able to perform some complex things like suggesting which products to buy to different customers depending on their buying behavior and much more, but you've never pictured yourself enrolling in a computer science class to study everything from scratch, then keep reading... Are you already tired about your business' poor performance or your limited ability to take advantage of modern technology to make your life easier because you don't understand the technology that every other successful person around you is leveraging every day? Have you tried using pre-built computer software to optimize your business but you never get the output you desire? Do you want to stop spending too much money on tech experts to assist you with tasks that you can do yourself with a little effort to learn, and increase efficiency? If so, then you've come to the right place. You see, having a successful online business or improving your knowledge about computer systems as regards to making accurate predictions for whatever goal doesn't have to be difficult- even if you are not ready to enroll for a computer science or IT course. In fact, it's easier than you think. One article published in Elite Data Science proves that machine learning can help us lead happier and healthier lives, especially with the recent breakthroughs in deep learning (that involves imitation of brain neurons). Another one published in Analytics Training asserts that learning machine learning is one of the best ways to guarantee success in many areas of the modern world, including business. Another article in Towards Data Science asserts that employing machine learning in manufacturing is the best way to improve efficiency, save money and time. That means that understanding machine learning, that tiny part of Artificial intelligence, can impact your daily life and businesses greatly. The question is.... Where do you even start? How do you build your understanding of machine learning to a point where you can start using it to make your life better? What are the critical things you need to learn about machine learning to get started as a beginner? What areas of your life can you apply your knowledge of machine learning? If you have these and other related questions, this book is for you so keep reading. Here's just a tiny fraction of what you'll discover in this book: The history of machine learning What machine learning can do for your business The machine learning algorithms How to develop a machine learning model from start to finish How neural networks work in machine learning The auto-encoders What you need to know about EM algorithm and how it is applied Logistic regression for machine learning The theory and setup in deep learning with tensorflow Natural language processing Data cleansing ...and much, much more! Take a second to imagine how you'd feel understanding how to can control a computer system to deliver output as you desire, without hiring an "expert", or at least understanding how computers make predictions. If you really want to find out how life would be once you know how to harness the power of modern technology to do anything you want, even if you are a complete beginner, Scroll up and click Buy Now With 1-Click or Buy Now to get started!
Download or read book Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2017-07-24 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.
Download or read book Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2018-05-25 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.