Download or read book The Hitchhiker s Guide to Responsible Machine Learning written by Przemys¿aw Biecek and published by Scientific Foundation Smarterpoland.PL. This book was released on 2022-02-13 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a unique entanglement of theory, examples and processes relevant to Responsible Machine Learning. You will find intuitions and examples for Interpretable Machine Learning (IML) and eXplainable Artificial Intelligence (XAI). Descriptions are supplemented by code snippets with examples for R with the use of randomForest, mlr3 and DALEX packages. Finally, the process is shown through a comic book describing the adventures of two characters, Beta and Bit. The interaction of these two shows the decisions that analysts often face, whether to try a different model, try another technique for exploration or look for other data -- questions like how to compare models or validate them. All examples are fully reproducible so that one can replay all adventures on a local desktop. Model development is a responsible and challenging task but also an exciting adventure. Sometimes textbooks focus only on the technical side, losing all the fun. Here we are going to have it all.

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 Explanatory Model Analysis written by Przemyslaw Biecek and published by CRC Press. This book was released on 2021-02-15 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.

Download or read book Human and Machine Learning written by Jianlong Zhou and published by Springer. This book was released on 2018-06-07 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.

Download or read book Practical Machine Learning written by Ally S. Nyamawe and published by CRC Press. This book was released on 2025-01-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field. It enables beginners to construct practical, real-world solutions powered by machine learning across diverse application domains. It demonstrates the fundamental techniques involved in data collection, integration, cleansing, transformation, development, and deployment of machine learning models. This book emphasizes the importance of integrating responsible and explainable AI into machine learning models, ensuring these principles are prioritized rather than treated as an afterthought. To support learning, this book also offers information on accessing additional machine learning resources such as datasets, libraries, pre-trained models, and tools for tracking machine learning models. This is a core resource for students and instructors of machine learning and data science looking for beginner-friendly material which offers real-world applications and takes ethical discussions into account.

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 Machine Learning for High Risk Applications written by Patrick Hall and published by "O'Reilly Media, Inc.". This book was released on 2023-04-17 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public. Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security Learn how to create a successful and impactful AI risk management practice Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework Engage with interactive resources on GitHub and Colab

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 Mastering Machine Learning A Comprehensive Guide to Success written by Rick Spair and published by Rick Spair. This book was released on with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to "Mastering Machine Learning: A Comprehensive Guide to Success." In this book, we embark on an exciting journey into the world of machine learning (ML), exploring its concepts, techniques, and practical applications. Whether you are a beginner taking your first steps into the field or an experienced practitioner seeking to deepen your knowledge, this comprehensive guide will equip you with the tools, strategies, and insights needed to succeed in the ever-evolving landscape of ML. Machine learning is a rapidly advancing field that has revolutionized industries and transformed the way we tackle complex problems. From personalized recommendations and speech recognition systems to autonomous vehicles and medical diagnostics, machine learning has become an integral part of our daily lives. Its ability to analyze vast amounts of data, identify patterns, and make predictions has paved the way for groundbreaking advancements across various domains. However, mastering machine learning requires more than just understanding the algorithms and techniques. It requires a holistic approach that encompasses data collection and preparation, exploratory data analysis, model building, evaluation, deployment, and continuous learning. It also demands a deep understanding of the ethical and social implications of machine learning, ensuring responsible and fair use of this powerful technology. In this book, we have carefully crafted 20 comprehensive chapters that cover a wide range of topics, from the fundamentals of machine learning to advanced techniques and future trends. Each chapter provides a deep dive into a specific aspect of machine learning, offering tips, recommendations, and strategies for success. You will learn about various algorithms, data preprocessing techniques, model evaluation methods, interpretability approaches, and much more. Throughout the book, we emphasize a practical approach to machine learning. Real-world examples, case studies, and hands-on exercises are incorporated to help you gain a deeper understanding of the concepts and apply them to your own projects. We believe that active learning and practical experience are crucial for mastering machine learning, and we encourage you to explore, experiment, and build your own models. While this book serves as a comprehensive guide, it is important to note that machine learning is a rapidly evolving field. New algorithms, techniques, and technologies are constantly emerging, and staying up-to-date with the latest advancements is essential. However, the principles and foundations discussed in this book will provide you with a solid framework to adapt and navigate the ever-changing landscape of machine learning. Whether you are an aspiring data scientist, a software engineer, a researcher, or a business professional, this book is designed to be your trusted companion in your journey to mastering machine learning. By the time you reach the end, you will have gained a deep understanding of the fundamental concepts, acquired practical skills for applying machine learning in real-world scenarios, and developed the mindset needed to tackle complex challenges and drive innovation. Get ready to embark on an exciting adventure into the world of machine learning. Let's begin our journey towards mastering machine learning and unlocking its full potential. Happy learning!

Download or read book Machine Learning written by Ryan Turner and published by Publishing Factory . This book was released on 2020-04-19 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you someone who is interested in how the next generation of machines can help you? Is Artificial Intelligence something to be feared, or do you imagine it that it will change our lives for the better? This book will provide the answers you need. Life is becoming ever more complex as we struggle to keep up with technology and use it to our best advantage. It is also more hectic and less certain, even in some of the mundane aspects of our lives, so that we are constantly trying to keep pace. New advancements in technology are paving the way to making life easier for billions and now things like Machine Learning and AI are changing the way we live. In this book, Machine Learning: The Ultimate Beginner’s Guide to Learn Machine Learning, Artificial Intelligence & Neural Networks Step by Step, you will see how this new technology continuously improves itself, can identify trends and patterns with ease and handles a wide variety of data, with chapters that explore: • Teaching the basic principles of Machine Learning • Why it is important and the many benefits that it provides • How Machine Learning differs from conventional programming • The fundamentals of algorithms • Challenges with Machine Learning and how you can easily overcome them • How it is going to change the future and make life easier • And much more… Machine Learning and AI are more than just science fiction. They are here now and undoubtedly will remain, improving and enhancing our lives in many ways, from the everyday to the vitally important. This book provides a platform that will give you a comprehensive understanding, that is second to none, of machine learning and its place in the world today. Get a copy now and see how Machine Learning will change your life!

Download or read book A Human s Guide to Machine Intelligence written by Kartik Hosanagar and published by Penguin. This book was released on 2020-03-10 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Wharton professor and tech entrepreneur examines how algorithms and artificial intelligence are starting to run every aspect of our lives, and how we can shape the way they impact us Through the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We've even delegated life-and-death decisions to algorithms--decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators--that is, like you and me. Hosanagar draws on his experiences designing algorithms professionally--as well as on history, computer science, and psychology--to explore how algorithms work and why they occasionally go rogue, what drives our trust in them, and the many ramifications of algorithmic decision-making. He examines episodes like Microsoft's chatbot Tay, which was designed to converse on social media like a teenage girl, but instead turned sexist and racist; the fatal accidents of self-driving cars; and even our own common, and often frustrating, experiences on services like Netflix and Amazon. A Human's Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user's guide to this first wave of practical artificial intelligence.

Download or read book Machine Learning and Robotics written by Albert Torres and published by Albert Torres. This book was released on 2021-04-19 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: !! 55% OFF for Bookstores!! NOW at 33.95 instead of 43.95 !! Buy it NOW and let your customers get addicted to this awesome book!

Download or read book Machine Learning written by Herbert Jones and published by Createspace Independent Publishing Platform. This book was released on 2018-06-23 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you're at the beginner stage and are looking to gain new knowledge about machine learning then keep reading... WARNING: Do not read this book if you're looking for a boring textbook containing a lot of dry math and programming lingo. Every day, someone is putting down a book on machine learning and giving up on learning about this revolutionary topic. How many of them miss out on furthering their career, and perhaps even the progress of our species...without even realizing? You see, most beginners make the same mistake when first delving into the topic of machine learning. They start off with a resource containing too many unrelatable facts, math, and programming lingo that will put them to sleep rather than ignite their passion. But that is about to change... This new book on machine learning will explain the concepts, methods and history behind machine learning, including how our computers became vastly more powerful but infinitely stupider than ever before and why every tech company and their grandmother want to keep track of us 24/7, siphoning data points from our electronic devices to be crunched by their programs that then become virtual crystal balls, predicting our thoughts before we even have them. Most of the book reads like science fiction because in a sense it is, far beyond what an average person would be willing to believe is happening. Here are some of the topics that are discussed in this book: What is machine learning? What's the point of machine learning? History of machine learning Neural networks Matching the human brain Artificial Intelligence AI in literature Talking, walking robots Self-driving cars Personal voice-activated assistants Data mining Social networks Big Data Shadow profiles Biometrics Self-replicating machines And much, much more! So if you want a book on machine learning that can cause some people to scream for more as oppose to falling asleep, click "add to cart"!

Download or read book Per Anhalter durch die Galaxis des verantwortungsvollen maschinellen Lernens written by Przemysław Biecek and published by Scientific Foundation Smarterpoland.PL. This book was released on 2023-02-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dieses Buch ist eine einzigartige Verflechtung von Theorie, Beispielen und Verfahren, die für verantwortungsvolles maschinelles Lernen relevant sind. Sie finden Intuitionen und Beispiele für Interpretable Machine Learning (IML) und eXplainable Artificial Intelligence (XAI). Die Beschreibungen werden durch Codeschnipsel mit Beispielen für R unter Verwendung der Pakete randomForest, mlr3 und DALEX ergänzt. Abschließend wird der Prozess anhand eines Comics gezeigt, der die Abenteuer von zwei Figuren, Beta und Bit, beschreibt. Die Interaktion dieser beiden zeigt die Entscheidungen, vor denen Analysten oft stehen, ob sie ein anderes Modell ausprobieren, eine andere Technik zur Erkundung ausprobieren oder nach anderen Daten suchen sollen - Fragen wie die, wie man Modelle vergleicht oder sie validiert. Alle Beispiele sind vollständig reproduzierbar, so dass man alle Erlebnisse auf einem lokalen Desktop nachspielen kann. Modellentwicklung ist eine verantwortungsvolle und anspruchsvolle Aufgabe, aber auch ein spannendes Abenteuer. Manchmal konzentrieren sich Lehrbücher nur auf die technische Seite und verlieren dabei den ganzen Spaß. Hier werden wir alles haben.

Download or read book Machine Learning written by Herbert Jones and published by Createspace Independent Publishing Platform. This book was released on 2018-10-10 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: 3 comprehensive manuscripts in 1 book Machine Learning: An Essential Guide to Machine Learning for Beginners Who Want to Understand Applications, Artificial Intelligence, Data Mining, Big Data and More Neural Networks: An Essential Beginners Guide to Artificial Neural Networks and their Role in Machine Learning and Artificial Intelligence Deep Learning: An Essential Guide to Deep Learning for Beginners Who Want to Understand How Deep Neural Networks Work and Relate to Machine Learning and Artificial Intelligence Every day, someone is putting down a book on machine learning and giving up on learning about this revolutionary topic. How many of them miss out on furthering their career, and perhaps even the progress of our species...without even realizing? You see, most beginners make the same mistake when first delving into the topic of machine learning. They start off with a resource containing too many unrelatable facts, math, and programming lingo that will put them to sleep rather than ignite their passion. But that is about to change... This new book on machine learning will explain the concepts, methods and history behind machine learning, including how our computers became vastly more powerful but infinitely stupider than ever before and why every tech company and their grandmother want to keep track of us 24/7, siphoning data points from our electronic devices to be crunched by their programs that then become virtual crystal balls, predicting our thoughts before we even have them. Most of the book reads like science fiction because in a sense it is, far beyond what an average person would be willing to believe is happening. Here are some of the topics that are discussed in part 1 of this book: What is machine learning? What's the point of machine learning? History of machine learning Neural networks Matching the human brain Artificial Intelligence AI in literature Talking, walking robots Self-driving cars Personal voice-activated assistants Data mining Social networks Big Data Shadow profiles Biometrics Self-replicating machines And much, much more! Here are some of the topics that are discussed in part 2 of this book: Programming a smart(er) computer Composition Giving neural networks legs to stand on The magnificent wetware Personal assistants Tracking users in the real world Self-driving neural networks Taking everyone's job Quantum leap in computing Attacks on neural networks Neural network war Ghost in the machine No backlash And Much, Much More Here are some of the topics that are discussed in part 3 of this book: Improving the Scientific Method How It All Started Appeasing the Rebellious Spirits Quantum Approach To Science The Replication Crisis Evolving the Machine Brain The Future of Deep Learning Medicine with the Help of a Digital Genie And Much, Much More So if you want to learn about machine learning, click "add to cart"!

Download or read book Machine Learning For Beginners written by Chris Neil and published by . This book was released on 2020-12-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you want to understand machine learning? How it works and how is correlated to artificial intelligence and deep learning? If yes, then keep reading... Machine Learning is based on mathematics, specifically statistics. It is a probabilistic discipline that began in the 1950s. Despite initial enthusiasm, research and development in Machine Learning languished for over 30 years, suffering from twin ills of a lack of data to work with and computers that were too slow to effectively work with what data they had. It is no accident Machine Learning is coming into its own over the last 10 years. Until we began creating and storing massive amounts of data about our world, ML was mostly an idea in the minds of statisticians. And until computers reached a level of speed and power where these massive data sets could be ingested in a reasonable amount of time, the revolution couldn't happen. But as we digitize information about our world and ourselves, and computers continue to increase in speed and capacity exponentially, the ability for Machine Learning to learn from our data grows in depth and accuracy. Looking to the future, we can see only more and more data collection about our world, faster computer chips, and data transfer, and more avenues for ML to develop in, to grow and learn, and to serve humanity. When most people think of machine learning, they either have no idea what it is, or they automatically think about artificial intelligence in the form of a robotic species that rivals humans. While these fascinating subspecies may one day exist as the result of machine learning developments, right now the primary focus is on how machine learning programs can become excellent at very specific tasks. Most machine learning technology is developed in such a way that it is excellent at performing one or, at most, two tasks. By focusing entire technology on one single task, they can ensure that it runs that task perfectly, and that it does not get confused between the tasks that it is trying to accomplish. While simple computing software like the one that runs your computer can easily run multiple programs at once with little chance of crashing, the technology that is used to run machine learning technology is far more complex. As researchers study it, they strive to keep the algorithms mostly separate, or specifically focused on completing just one goal, on minimizing room for error. It is likely that as we become more familiar with machine learning technology and more educated in the algorithms, we will start to see more and more machines completing multiple tasks, rather than just one. At this point, that is the long-term goal for many scientists who want to see these machines becoming more efficient, and requiring less hardware. After all, the hardware used to run some of these machines is not always the greenest technology, so the fewer hardware casings that technology needs to be stored in, the less of a footprint the technology sector will have on the planet. This book aims to educate you on the truth about machine learning. This book gives a comprehensive guide on the following: What is Machine Learning? Machine Learning Categories Sectors and Industries that use Machine Learning Fundamental Algorithms Regression Analysis Benefits of Machine Learning Deep Learning Deep Neural Network Big Data Analytics Big Data Analysis Tools How Companies Use Big Data Data Mining and Applications ... AND MORE!!! What are you waiting for? Click buy now!!!!!