Simplified Machine Learning
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Simplified Machine Learning
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Author : Dr. Pooja Sharma
language : en
Publisher: BPB Publications
Release Date : 2024-06-15
Simplified Machine Learning written by Dr. Pooja Sharma and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-15 with Computers categories.
Explore the world of Artificial Intelligence with a deep understanding of Machine Learning concepts and algorithms KEY FEATURES ● A detailed study of mathematical concepts, Machine Learning concepts, and techniques. ● Discusses methods for evaluating model performances and interpreting results. ● Explores all types of Machine Learning (supervised, unsupervised, reinforcement, association rule mining, artificial neural network) in detail. ● Comprises numerous review questions and programming exercises at the end of every chapter. DESCRIPTION "Simplified Machine Learning" is a comprehensive guide that navigates readers through the intricate landscape of Machine Learning, offering a balanced blend of theory, algorithms, and practical applications. The first section introduces foundational concepts such as supervised and unsupervised learning, regression, classification, clustering, and feature engineering, providing a solid base in Machine Learning theory. The second section explores algorithms like decision trees, support vector machines, and neural networks, explaining their functions, strengths, and limitations, with a special focus on deep learning, reinforcement learning, and ensemble methods. The book also covers essential topics like model evaluation, hyperparameter tuning, and model interpretability. The final section transitions from theory to practice, equipping readers with hands-on experience in deploying models, building scalable systems, and understanding ethical considerations. By the end, readers will be able to leverage Machine Learning effectively in their respective fields, armed with practical skills and a strategic approach to problem-solving. WHAT YOU WILL LEARN ● Solid foundation in Machine Learning principles, algorithms, and methodologies. ● Implementation of Machine Learning models using popular libraries like NumPy, Pandas, PyTorch, or scikit-learn. ● Knowledge about selecting appropriate models, evaluating their performance, and tuning hyperparameters. ● Techniques to pre-process and engineer features for Machine Learning models. ● To frame real-world problems as Machine Learning tasks and apply appropriate techniques to solve them. WHO THIS BOOK IS FOR This book is designed for a diverse audience interested in Machine Learning, a core branch of Artificial Intelligence. Its intellectual coverage will benefit students, programmers, researchers, educators, AI enthusiasts, software engineers, and data scientists. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Data Pre-processing 3. Supervised Learning: Regression 4. Supervised Learning: Classification 5. Unsupervised Learning: Clustering 6. Dimensionality Reduction and Feature Selection 7. Association Rule Mining 8. Artificial Neural Network 9. Reinforcement Learning 10. Project Appendix Bibliography
Demystifying Artificial Intelligence
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Author : Prashant Kikani
language : en
Publisher: BPB Publications
Release Date : 2021-01-05
Demystifying Artificial Intelligence written by Prashant Kikani and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-05 with Computers categories.
Learn AI & Machine Learning from the first principles. KEY FEATURESÊÊ _ Explore how different industries are using AI and ML for diverse use-cases. _ Learn core concepts of Data Science, Machine Learning, Deep Learning and NLP in an easy and intuitive manner. _ Cutting-edge coverage on use of ML for business products and services. _ Explore how different companies are monetizing AI and ML technologies. _ Learn how you can start your own journey in the AI field from scratch. DESCRIPTION AI and machine learning (ML) are probably the most fascinating technologies of the 21st century. AI is literally in every industry now. From medical to climate change, education to sport, finance to entertainment, AI is disrupting every industry as we know. So, the basic knowledge of AI/ML becomes mandatory for everyone. This book is your first step to start the journey in this field. Along with basic concepts of fields, like machine learning, deep learning and NLP, we will also explore how big companies are using these technologies to deliver greater user experience and earning millions of dollars in profit. Also, we will see how the owners of small- or medium-sized businesses can leverage and integrate these technologies with their products and services. Leveraging AI and ML can become that competitive moat which can differentiate the product from others. In this book, you will learn the root concepts of AI/ML and how these inanimate machines can actually become smarter than the humans at a few tasks, and how companies are using AI and how you can leverage AI to earn profits. WHAT YOU WILL LEARN Ê _ Core concepts of data science, machine learning, deep learning and NLP in simple and intuitive words. _ How you can leverage and integrate AI technologies in your business to differentiate your product in the market. _ The limitations of traditional non-tech businesses and how AI can bridge those gaps to increase revenues and decrease costs. _ How AI can help companies in launching new products, improving existing ones and automating mundane processes. _ Explore how big tech companies are using AI to automate different tasks and providing unique product experiences to their users. WHO THIS BOOK IS FORÊÊ This book is for anyone who is curious about this fascinating technology and how it really works at its core. It is also beneficial to those who want to start their career in AI/ ML. TABLE OF CONTENTSÊ 1. Introduction 2. Going deeper in ML concepts 3. Business perspective of AI 4. How to get started and pitfalls to avoid
Scikit Learn Machine Learning Simplified
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Author : Raul Garreta
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-11-10
Scikit Learn Machine Learning Simplified written by Raul Garreta and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-10 with Computers categories.
Implement scikit-learn into every step of the data science pipeline About This Book Use Python and scikit-learn to create intelligent applications Discover how to apply algorithms in a variety of situations to tackle common and not-so common challenges in the machine learning domain A practical, example-based guide to help you gain expertise in implementing and evaluating machine learning systems using scikit-learn Who This Book Is For If you are a programmer and want to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this is the course for you. No previous experience with machine-learning algorithms is required. What You Will Learn Review fundamental concepts including supervised and unsupervised experiences, common tasks, and performance metrics Classify objects (from documents to human faces and flower species) based on some of their features, using a variety of methods from Support Vector Machines to Naive Bayes Use Decision Trees to explain the main causes of certain phenomena such as passenger survival on the Titanic Evaluate the performance of machine learning systems in common tasks Master algorithms of various levels of complexity and learn how to analyze data at the same time Learn just enough math to think about the connections between various algorithms Customize machine learning algorithms to fit your problem, and learn how to modify them when the situation calls for it Incorporate other packages from the Python ecosystem to munge and visualize your dataset Improve the way you build your models using parallelization techniques In Detail Machine learning, the art of creating applications that learn from experience and data, has been around for many years. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility; moreover, within the Python data space, scikit-learn is the unequivocal choice for machine learning. The course combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. The course starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets. You will learn to incorporate machine learning in your applications. Ranging from handwritten digit recognition to document classification, examples are solved step-by-step using scikit-learn and Python. By the end of this course you will have learned how to build applications that learn from experience, by applying the main concepts and techniques of machine learning. Style and Approach Implement scikit-learn using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. This is a practical course, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of scikit-learn.
Scikit Learn
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Author : Raúl Garreta
language : en
Publisher:
Release Date : 2017-11-10
Scikit Learn written by Raúl Garreta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-10 with Computers categories.
Implement scikit-learn into every step of the data science pipelineAbout This Book* Use Python and scikit-learn to create intelligent applications* Discover how to apply algorithms in a variety of situations to tackle common and not-so common challenges in the machine learning domain* A practical, example-based guide to help you gain expertise in implementing and evaluating machine learning systems using scikit-learnWho This Book Is ForIf you are a programmer and want to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this is the course for you. No previous experience with machine-learning algorithms is required.What You Will Learn* Review fundamental concepts including supervised and unsupervised experiences, common tasks, and performance metrics* Classify objects (from documents to human faces and flower species) based on some of their features, using a variety of methods from Support Vector Machines to Naive Bayes* Use Decision Trees to explain the main causes of certain phenomena such as passenger survival on the Titanic* Evaluate the performance of machine learning systems in common tasks* Master algorithms of various levels of complexity and learn how to analyze data at the same time* Learn just enough math to think about the connections between various algorithms* Customize machine learning algorithms to fit your problem, and learn how to modify them when the situation calls for it* Incorporate other packages from the Python ecosystem to munge and visualize your dataset* Improve the way you build your models using parallelization techniquesIn DetailMachine learning, the art of creating applications that learn from experience and data, has been around for many years. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility; moreover, within the Python data space, scikit-learn is the unequivocal choice for machine learning. The course combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. The course starts by walking through different methods to prepare your data-be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives-be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets. You will learn to incorporate machine learning in your applications. Ranging from handwritten digit recognition to document classification, examples are solved step-by-step using scikit-learn and Python. By the end of this course you will have learned how to build applications that learn from experience, by applying the main concepts and techniques of machine learning.Style and ApproachImplement scikit-learn using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. This is a practical course, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of scikit-learn.
Machine Learning For Dummies
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Author : John Paul Mueller
language : en
Publisher: John Wiley & Sons
Release Date : 2016-05-31
Machine Learning For Dummies written by John Paul Mueller and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-31 with Computers categories.
Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!
Practical Deep Reinforcement Learning With Python
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Author : Ivan Gridin
language : en
Publisher: BPB Publications
Release Date : 2022-07-15
Practical Deep Reinforcement Learning With Python written by Ivan Gridin and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-15 with Computers categories.
Introducing Practical Smart Agents Development using Python, PyTorch, and TensorFlow KEY FEATURES ● Exposure to well-known RL techniques, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical. ● Hands-on experience with TensorFlow and PyTorch on Reinforcement Learning projects. ● Everything is concise, up-to-date, and visually explained with simplified mathematics. DESCRIPTION Reinforcement learning is a fascinating branch of AI that differs from standard machine learning in several ways. Adaptation and learning in an unpredictable environment is the part of this project. There are numerous real-world applications for reinforcement learning these days, including medical, gambling, human imitation activity, and robotics. This book introduces readers to reinforcement learning from a pragmatic point of view. The book does involve mathematics, but it does not attempt to overburden the reader, who is a beginner in the field of reinforcement learning. The book brings a lot of innovative methods to the reader's attention in much practical learning, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical methods. While you understand these techniques in detail, the book also provides a real implementation of these methods and techniques using the power of TensorFlow and PyTorch. The book covers some enticing projects that show the power of reinforcement learning, and not to mention that everything is concise, up-to-date, and visually explained. After finishing this book, the reader will have a thorough, intuitive understanding of modern reinforcement learning and its applications, which will tremendously aid them in delving into the interesting field of reinforcement learning. WHAT YOU WILL LEARN ● Familiarize yourself with the fundamentals of Reinforcement Learning and Deep Reinforcement Learning. ● Make use of Python and Gym framework to model an external environment. ● Apply classical Q-learning, Monte Carlo, Policy Gradient, and Thompson sampling techniques. ● Explore TensorFlow and PyTorch to practice the fundamentals of deep reinforcement learning. ● Design a smart agent for a particular problem using a specific technique. WHO THIS BOOK IS FOR This book is for machine learning engineers, deep learning fanatics, AI software developers, data scientists, and other data professionals eager to learn and apply Reinforcement Learning to ongoing projects. No specialized knowledge of machine learning is necessary; however, proficiency in Python is desired. TABLE OF CONTENTS Part I 1. Introducing Reinforcement Learning 2. Playing Monopoly and Markov Decision Process 3. Training in Gym 4. Struggling With Multi-Armed Bandits 5. Blackjack in Monte Carlo 6. Escaping Maze With Q-Learning 7. Discretization Part II. Deep Reinforcement Learning 8. TensorFlow, PyTorch, and Your First Neural Network 9. Deep Q-Network and Lunar Lander 10. Defending Atlantis With Double Deep Q-Network 11. From Q-Learning to Policy-Gradient 12. Stock Trading With Actor-Critic 13. What Is Next?
Neural Networks For Beginners
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Author : Russel R Russo
language : en
Publisher:
Release Date : 2019-11-06
Neural Networks For Beginners written by Russel R Russo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-06 with categories.
Do you want to understand Neural Networks and learn everything about them but it looks like it is an exclusive club? Are you fascinated by Artificial Intelligence but you think that it would be too difficult for you to learn? If you think that Neural Networks and Artificial Intelligence are the present and, even more, the future of technology, and you want to be part of it... well you are in the right place, and you are looking at the right book. If you are reading these lines you have probably already noticed this: Artificial Intelligence is all around you. Your smartphone that suggests you the next word you want to type, your Netflix account that recommends you the series you may like or Spotify's personalised playlists. This is how machines are learning from you in everyday life. And these examples are only the surface of this technological revolution. Either if you want to start your own AI entreprise, to empower your business or to work in the greatest and most innovative companies, Artificial Intelligence is the future, and Neural Networks programming is the skill you want to have. The good news is that there is no exclusive club, you can easily (if you commit, of course) learn how to program and use neural networks, and to do that Neural Networks for Beginners is the perfect way. In this book you will learn: The types and components of neural networks The smartest way to approach neural network programming Why Algorithms are your friends The "three Vs" of Big Data (plus two new Vs) How machine learning will help you making predictions The three most common problems with Neural Networks and how to overcome them Even if you don't know anything about programming, Neural Networks is the perfect place to start now. Still, if you already know about programming but not about how to do it in Artificial Intelligence, neural networks are the next thing you want to learn. And Neural Networks for Beginners is the best way to do it. Download Neural Network for Beginners now to get the best start for your journey to Artificial Intelligence. Scroll to the top of the page and click the BUY NOW button.
Artificial Intelligence Simplified
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Author : Binto George
language : en
Publisher: CSTrends LLP
Release Date : 2016-01-08
Artificial Intelligence Simplified written by Binto George and has been published by CSTrends LLP this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-08 with Computers categories.
The book introduces key Artificial Intelligence (AI) concepts in an easy-to-read format with examples and illustrations. A complex, long, overly mathematical textbook does not always serve the purpose of conveying the basic AI concepts to most people. Someone with basic knowledge in Computer Science can have a quick overview of AI (heuristic searches, genetic algorithms, expert systems, game trees, fuzzy expert systems, natural language processing, super intelligence, etc.) with everyday examples. If you are taking a basic AI course and find the traditional AI textbooks intimidating, you may choose this as a “bridge” book, or as an introductory textbook. For students, there is a lower priced edition (ISBN 978-1944708016) of the same book. Published by CSTrends LLP.
Machine Learning
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Author : Anthony Aline
language : en
Publisher:
Release Date : 2019-03-29
Machine Learning written by Anthony Aline and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-29 with categories.
★★Buy the Paperback Version of this Book and get the Kindle Book version for FREE ★★ Artificial Intelligence, and in particular, Machine Learning is here today and it is shaping our world. It is shaping and simplifying the way we live, work, travel and communicate. Hence it is important for you to understand what it is and how it works. This guide has been designed to help you gain an understanding of machine learning, artificial intelligence and big data in a simple way. We will walk you step-by-step into the world of Machine Learning. You will have the opportunity to develop new skills and improve your understanding of this challenging yet lucrative sub-field of machine learning. The content presented in this eBook has been prepared for the total beginner and it doesn't matter who you are or whatever job you are currently doing. This e-book will also be of great benefit to you if you are a business owner. No matter who you are or what you are doing currently, Machine learning can be understood by anyone. This E-book is recommended for you so long you have an interest in machine learning. This introductory guide on machine learning is fun and exciting, but at the same time we dive deep into Machine Learning in a structured way. This guide us easy to read and after reading the whole guide you should have in-depth understanding of the following: Gain an in-depth understanding of Machine Learning, Data Science, Neural Networks, Artificial Intelligence and Neural networks Have a great understanding of many Machine Learning models Know about how accurate prediction are made using machine learning Know the myth various machine learning myths Have a great understanding of how giant companies like Amazon and Netflix, Facebook and twitter are using machine learning Understand the applications of machine learning Scroll Up and Click the Buy Now Button!
Grokking Machine Learning
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Author : Luis Serrano
language : en
Publisher: Simon and Schuster
Release Date : 2021-12-28
Grokking Machine Learning written by Luis Serrano and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-28 with Computers categories.
Discover valuable machine learning techniques you can understand and apply using just high-school math. In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations. About the book Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data. What's inside Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets About the reader For readers who know basic Python. No machine learning knowledge necessary. About the author Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple. Table of Contents 1 What is machine learning? It is common sense, except done by a computer 2 Types of machine learning 3 Drawing a line close to our points: Linear regression 4 Optimizing the training process: Underfitting, overfitting, testing, and regularization 5 Using lines to split our points: The perceptron algorithm 6 A continuous approach to splitting points: Logistic classifiers 7 How do you measure classification models? Accuracy and its friends 8 Using probability to its maximum: The naive Bayes model 9 Splitting data by asking questions: Decision trees 10 Combining building blocks to gain more power: Neural networks 11 Finding boundaries with style: Support vector machines and the kernel method 12 Combining models to maximize results: Ensemble learning 13 Putting it all in practice: A real-life example of data engineering and machine learning