Artificial Intelligence And Machine Learning A Precise Book To Learn Basics

Artificial Intelligence and Machine Learning - A Precise Book to Learn Basics Table of Contents 1. Introduction to Artificial Intelligence and Machine Learning 1.1 What is Artificial Intelligence? 1.2 The Evolution of Artificial Intelligence 1.3 What is Machine Learning? 1.4 How Machine Learning Differs from Traditional Programming 1.5 The Importance of Artificial Intelligence and Machine Learning 2. Foundations of Machine Learning 2.1 Supervised Learning 2.1.1 Linear Regression 2.1.2 Logistic Regression 2.1.3 Decision Trees 2.2 Unsupervised Learning 2.2.1 Clustering 2.2.2 Dimensionality Reduction 2.3 Reinforcement Learning 2.3.1 Markov Decision Process 2.3.2 Q-Learning 3. Neural Networks and Deep Learning 3.1 Introduction to Neural Networks 3.2 Artificial Neural Networks 3.2.1 The Perceptron 3.2.2 Multi-Layer Perceptron 3.3 Convolutional Neural Networks 3.4 Recurrent Neural Networks 3.5 Generative Adversarial Networks 4. Natural Language Processing 4.1 Introduction to Natural Language Processing 4.2 Preprocessing and Text Representation 4.3 Sentiment Analysis 4.4 Named Entity Recognition 4.5 Text Summarization 5. Computer Vision 5.1 Introduction to Computer Vision 5.2 Image Processing 5.3 Object Detection 5.4 Image Segmentation 5.5 Face Recognition 6. Reinforcement Learning Applications 6.1 Reinforcement Learning in Robotics 6.2 Reinforcement Learning in Games 6.3 Reinforcement Learning in Finance 6.4 Reinforcement Learning in Healthcare 7. Ethics and Social Implications of Artificial Intelligence 7.1 Bias in Artificial Intelligence 7.2 The Future of Work 7.3 Privacy and Security 7.4 The Impact of AI on Society 8. Machine Learning Infrastructure 8.1 Cloud Infrastructure for Machine Learning 8.2 Distributed Machine Learning 8.3 DevOps for Machine Learning 9. Machine Learning Tools 9.1 Introduction to Machine Learning Tools 9.2 Python Libraries for Machine Learning 9.3 TensorFlow 9.4 Keras 9.5 PyTorch 10. Building and Deploying Machine Learning Models 10.1 Building a Machine Learning Model 10.2 Hyperparameter Tuning 10.3 Model Evaluation 10.4 Deployment Considerations 11. Time Series Analysis and Forecasting 11.1 Introduction to Time Series Analysis 11.2 ARIMA 11.3 Exponential Smoothing 11.4 Deep Learning for Time Series 12. Bayesian Machine Learning 12.1 Introduction to Bayesian Machine Learning 12.2 Bayesian Regression 12.3 Bayesian Classification 12.4 Bayesian Model Averaging 13. Anomaly Detection 13.1 Introduction to Anomaly Detection 13.2 Unsupervised Anomaly Detection 13.3 Supervised Anomaly Detection 13.4 Deep Learning for Anomaly Detection 14. Machine Learning in Healthcare 14.1 Introduction to Machine Learning in Healthcare 14.2 Electronic Health Records 14.3 Medical Image Analysis 14.4 Personalized Medicine 15. Recommender Systems 15.1 Introduction to Recommender Systems 15.2 Collaborative Filtering 15.3 Content-Based Filtering 15.4 Hybrid Recommender Systems 16. Transfer Learning 16.1 Introduction to Transfer Learning 16.2 Fine-Tuning 16.3 Domain Adaptation 16.4 Multi-Task Learning 17. Deep Reinforcement Learning 17.1 Introduction to Deep Reinforcement Learning 17.2 Deep Q-Networks 17.3 Actor-Critic Methods 17.4 Deep Reinforcement Learning Applications 18. Adversarial Machine Learning 18.1 Introduction to Adversarial Machine Learning 18.2 Adversarial Attacks 18.3 Adversarial Defenses 18.4 Adversarial Machine Learning Applications 19. Quantum Machine Learning 19.1 Introduction to Quantum Computing 19.2 Quantum Machine Learning 19.3 Quantum Computing Hardware 19.4 Quantum Machine Learning Applications 20. Machine Learning in Cybersecurity 20.1 Introduction to Machine Learning in Cybersecurity 20.2 Intrusion Detection 20.3 Malware Detection 20.4 Network Traffic Analysis 21. Future Directions in Artificial Intelligence and Machine Learning 21.1 Reinforcement Learning in Real-World Applications 21.2 Explainable Artificial Intelligence 21.3 Quantum Machine Learning 21.4 Autonomous Systems 22. Conclusion 22.1 Summary 22.2 Key Takeaways 22.3 Future Directions 22.4 Call to Action

If you have ever wondered what drives the many tools we use every day, then keep reading. The Fourth Industrial Revolution is led by Artificial Intelligence technology and setting the humankind for a global social transformation. The powerful applications of AI have already transformed our daily lives. Tools such as virtual personal and home assistants (like Siri in Apple Pods and Alexa in Amazon Echo) have become everyday usage products. Artificial Intelligence and Machine Learning are closely related. They have become an important part of scientific study. Not only does it involve the study of statistical models and algorithms, but also the systems used for task performance. Our aim with this book is to provide you a 360 view of the fundamentals and importance of Machine Learning Technology for the beginners' level. You Will Learn: The Fundamentals and Concepts of Artificial Intelligence in 2020 The Technology behind AI, and its Rapid growth and Evolution The Advantages and Disadvantages of Artificial Intelligence How AI Helps Business The Importance of Deep Learning Today How the Fields of Data Science and Its Many Applications Helps Your Business Computer Science and Its Applications in Real World Basic Terminology Used in Artificial Intelligence As we cover the basics of Machine Learning and Artificial Intelligence, you will be glad to know that it can be understood and processed on the beginners' level. Even though it may seem to have some big words. Would You Like to Know More? Get This book Today to know how Machine Learning is changing our world.

Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.

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Are you fascinated about machine learning and AI and you don't know where to start? Have you ever heard people talking about Machine Learning but you only have a vague idea of the actual meaning? Do you want to understand how machine learning could simplify your daily life? Imagine a world where computing systems understand people and the world around us them to a point where they can notice patterns, collect data, interpret it and give recommendations to solve real world problems with high level of precision. It sounds like science fiction but it is happening in healthcare, agriculture, cyber security, facial recognition, targeting and retargeting customers in online advertising, recommending specific products, stories, videos, text etc., self-driving cars, real time pricing, predicting human behavior and much more. Now imagine you being one of the people behind the code; the people who get these advanced systems to work the way they do. Would it be a dream come true for you? By virtue that you are reading this, it is clear that you have some special liking for this advanced tech and would want to learn how you can be one of the people behind the code. Even if not, you probably want to be able to understand the inner workings of these systems. The concept may sound extremely out there and advanced but it won't be if you follow this guide, which takes an easy to follow, beginner friendly language to help you to understand the ins and outs of machine learning! Here is a summary of what this book will teach you: The basics of machine learning, including what it is, how machine learning has evolved over the years, the application of machine learning in today's world and the future of machine learning How machine learning is beneficial in today's world The different approaches to machine learning, including unsupervised, supervised, reinforcement learning method, semi-supervised machine learning and many others The concept of big data analysis, including what is big data, why big data is important, the application of big data in today's world as well as the different data analysis tools that you can use The link between big data and machine learning The different machine learning algorithms, including what machine-learning algorithms are and how and when the different learning algorithms are used The concept of artificial neural networks, including how they work, when to use neural networks and more How decision trees are used in machine learning, including what decision trees are (in respect to machine learning), how they work, how the decision tree is read, the different nodes in decision trees and when to use them The ins and outs of linear and logistic regression in machine learning, including what linear regression is, different types of regression, how linear regression works, how linear regression is used and much more And much more! Even if this is your first encounter with the concept of machine learning, this book will uncover everything you need to know to master machine learning and possibly get started in this field of advanced computing knowing very well what you are venturing into. And the good thing is that the book takes a beginner friendly approach to help you to apply what you learn right away! Would You Like To Know More? Click Buy Now With 1-Click or Buy Now to get started!

If you want to learn key AI concepts to get you quickly up to speed with all things AI, then keep reading Two manuscripts in one book: Artificial Intelligence: What You Need to Know About Machine Learning, Robotics, Deep Learning, Recommender Systems, Internet of Things, Neural Networks, Reinforcement Learning, and Our Future Internet of Things: What You Need to Know About IoT, Big Data, Predictive Analytics, Artificial Intelligence, Machine Learning, Cybersecurity, Business Intelligence, Augmented Reality and Our Future This book covers everything from machine learning to robotics and the internet of things. You can use it as a nifty guidebook whenever you come across news headlines that talk about some new advancement in AI by Google or Facebook. By the time you finish reading, you will be aware of what artificial neural networks are, how gradient descent and back propagation work, and what deep learning is. You will also learn a comprehensive history of AI, from the first invention of automations in antiquity to the driver-less cars of today. In part 1 of this book, you will: Understand how machines can "think" and how they learn Learn the five reasons why experts are warning us about AI research Find the answers to the top six myths of artificial intelligence Learn what neural networks are and how they work, the "brains" of machine learning Understand reinforcement learning and how it is used to teach machine learning systems through experience Become up-to-date with the current state-of-the-art artificial intelligence methods that use deep learning Learn the basics of recommender systems Expand your current view of machines and what is possible with modern robotics Enter the vast world of the internet of things technologies Find out why AI is the new business degree And much, much more! Some of the topics covered in part 2 of this book include: Origins of IoT IoT Security Ethical Hacking Internet of Things Under The Cushy Foot of Tech Giants The Power of Infinite Funds IoT Toys Bio-robotics Predictive Analytics Machine Learning Artificial Intelligence Cybersecurity Big Data Business Intelligence Augmented Reality Virtual Reality Our Future And much, much more If you want to learn more about the artificial intelligence and internet of things, then scroll up and click "add to cart"!

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!