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Fundamentals of Machine Learning Using Python by Euan Russano,Elaine Ferreira Avelino Pdf
Fundamentals of Machine Learning discusses the basics of python, use of python in computing and provides a general outlook on machine learning. This book provides an insight into concepts such as linear regression with one variable, linear algebra, and linear regression with multiple inputs. The classification with logistics regression model, regularization, neural networks, decision trees are explained in this book. The introduction to several concepts of machine learning such as component analysis, classification using k-Nearest Algorithm, k Means Clustering, computing with Tensor flow and natural language processing have been explained. This book explains the fundamental concepts of machine learning.
Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido Pdf
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron Pdf
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
Fundamentals of Machine Learning by Thomas Trappenberg Pdf
Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life. This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and even its broader impact on our society. The book is written in a style that strikes a balance between brevity of explanation, rigorous mathematical argument, and outlines principle ideas. At the same time, it provides a comprehensive overview of a variety of methods and their application within this field. This includes an introduction to Bayesian approaches to modeling, as well as deep learning. Writing small programs to apply machine learning techniques is made easy by high level programming systems, and this book shows examples in Python with the machine learning libraries 'sklearn' and 'Keras'. The first four chapters concentrate on the practical side of applying machine learning techniques. The following four chapters discuss more fundamental concepts that includes their formulation in a probabilistic context. This is followed by two more chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society. Fundamentals of Machine Learning provides a brief and accessible introduction to this rapidly growing field, one that will appeal to students and researchers across computer science and computational neuroscience, as well as the broader cognitive sciences.
Fundamentals of Deep Learning by Nikhil Buduma,Nicholas Locascio Pdf
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Learn the fundamentals of reinforcement learning
With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new level Key FeaturesExplore scikit-learn uniform API and its application into any type of modelUnderstand the difference between supervised and unsupervised modelsLearn the usage of machine learning through real-world examplesBook Description As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem. The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters. By the end of this book, you will have gain all the skills required to start programming machine learning algorithms. What you will learnUnderstand the importance of data representationGain insights into the differences between supervised and unsupervised modelsExplore data using the Matplotlib libraryStudy popular algorithms, such as k-means, Mean-Shift, and DBSCANMeasure model performance through different metricsImplement a confusion matrix using scikit-learnStudy popular algorithms, such as Naïve-Bayes, Decision Tree, and SVMPerform error analysis to improve the performance of the modelLearn to build a comprehensive machine learning programWho this book is for Machine Learning Fundamentals is designed for developers who are new to the field of machine learning and want to learn how to use the scikit-learn library to develop machine learning algorithms. You must have some knowledge and experience in Python programming, but you do not need any prior knowledge of scikit-learn or machine learning algorithms.
This book aspires young graduates and programmers to become AI engineers and enter the world of artificial intelligence by combining powerful Python programming with artificial intelligence. Beginning with the fundamentals of Python programming, the book gradually progresses to machine learning, where readers learn to implement Python in developing predictive models. The book provides a clear and accessible explanation of machine learning, incorporating practical examples and exercises that strengthen understanding. We go deep into deep learning, another vital component of AI. Readers gain a thorough understanding of how Python's frameworks and libraries can be used to create sophisticated neural networks and algorithms, which are required for tasks such as image and speech recognition. Natural Language Processing is also covered in the book, with fundamental concepts and techniques for interpreting and generating human-like language covered. The book's focus on computer vision and reinforcement learning is distinctive, presenting these cutting-edge AI fields in an approachable manner. Readers will learn how to use Python's intuitive programming paradigm to create systems that interpret visual data and make intelligent decisions based on environmental interactions. The book focuses on ethical AI development and responsible programming, emphasizing the importance of developing AI that is fair, transparent, and accountable. Each chapter is designed to improve learning by including practical examples, case studies, and exercises that provide hands-on experience. This book is an excellent starting point for anyone interested in becoming an AI engineer, providing the necessary foundational knowledge and skills to delve into the fascinating world of artificial intelligence. Key Learnings Explore Python basics and AI integration for real-world application and career advancement. Experience the power of Python in AI with practical machine learning techniques. Practice Python's deep learning tools for innovative AI solution development. Dive into NLP with Python to revolutionize data interpretation and communication strategies. Simple yet practical understanding of reinforcement learning for strategic AI decision making. Uncover ethical AI development and frameworks, and concepts of responsible and trustworthy AI. Harness Python's capabilities for creating AI applications with a focus on fairness and bias. Table of Content Introduction to Artificial Intelligence Python for AI Data as Fuel for AI Machine Learning Foundation Essentials of Deep Learning NLP and Computer Vision Hands-on Reinforcement Learning Ethics to AI
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Python Machine Learning For Beginners by Finn Sanders Pdf
Imagine a world where you can make a computer program learn for itself? What if it could recognize who is in a picture or the exact websites that you want to look for when you type it into the program? What if you were able to create any kind of program that you wanted, even as a beginner programmer, without all of the convoluted codes and other information that makes your head spin? This is actually all possible. The programs that were mentioned before are all a part of machine learning. This is a breakthrough in the world of information technology, which allows the computer to learn how to behave, rather than asking the programmer to think of every single instance that may show up with their user ahead of time. it is taking over the world, and you may be using it now, without even realizing it. If you have used a search engine, worked with photo recognition, or done speech recognition devices on your phone, then you have worked with machine learning. And if you combine it with the Python programming language, it is faster, more powerful, and easier (even for beginners) to create your own programs today. Python is considered the ultimate coding language for beginners, but once you start to use it, you will never be able to tell. Many of the best programs out there use this language behind them, and if you are a beginner who is ready to learn, this is a great place to start. If you have a program in mind, or you just want to be able to get some programming knowledge and learn more about the power that comes behind it, then this is the guidebook for you. ★★Some of the topics that we will discuss include★★ ♦ The Fundamentals of Machine Learning, Deep learning, And Neural Networks ♦ How To Set Up Your Environment And Make Sure That Python, TensorFlow And Scikit-Learn Work Well For You ♦ How To Master Neural Network Implementation Using Different Libraries ♦ How Random Forest Algorithms Are Able To Help Out With Machine Learning ♦ How To Uncover Hidden Patterns And Structures With Clustering ♦ How Recurrent Neural Networks Work And When To Use ♦ The Importance Of Linear Classifiers And Why They Need To Be Used In Machine Learning ♦ And Much More! This guidebook is going to provide you with the information you need to get started with Python Machine Learning. If you have an idea for a great program, but you don't have the technical knowledge to make it happen, then this guidebook will help you get started. Machine learning has the capabilities, and Python has the ease, to help you, even as a beginner, create any product that you would like. If you want to learn more about how to make the best programs with Python Machine learning, buy the book today!
Discover the Incredible World of Machine Learning With This Amazing Guide Do you want to understand machine learning, but it all looks too daunting and complex? Afraid to open the 'pandora's box' and waste hours searching for answers? Then keep reading Written with the beginner in mind, this powerful guide breaks down everything you need to know about machine learning and Python in a simple, easy-to-understand way. So many other books make machine learning look impossible to understand and even harder to master - but now you can familiarize yourself with this incredible technology like never before! With a detailed and concise overview of the fundamentals, along with the challenges and limitations currently being tackled by the pros, inside this comprehensive guide you will Learn the Fundamentals of Machine Learning which Are Being Developed and Advanced with Python Master the Nuances of 12 of the Most Popular and Widely-Used Machine Learning Algorithms, in a Language that Requires No Prior Background in Python Discover the Details of the Supervised, Unsupervised, and Reinforcement Algorithms, which Serve as the Skeleton of Hundreds of Machine Learning Algorithms Being Developed Every Day Become Familiar with Data Science Technology, an Umbrella Term Used for the Cutting-Edge Technologies of Today Dive Into the Functioning of Scikit-Learn Library and Develop Machine Learning Models, with a Detailed Walkthrough and Open Source Database using Illustrations and actual Python Code Understand the Entire Process of Creating Neural Network Models on TensorFlow, Using Open Source Data Sets and real Python Code Uncover the Secrets of the Most Critical Aspect of Developing a Machine Learning Model - Data Pre-Processing and Training/Testing Subsets With a wealth of tips and tricks, along with invaluable advice guaranteed to help you with your machine learning journey, this book is a powerful and revolutionary tool for creating, developing, and using machine learning. From understanding the Python language to creating data sets and building neural networks, now you can become the master of machine learning with this incredible guide! So what are you waiting for? Buy Now and Join the Millions of People Using Machine Learning Today!
Ready to discover the Machine Learning world? Machine learning paves the path into the future and it’s powered by Python. All industries can benefit from machine learning and artificial intelligence whether we’re talking about private businesses, healthcare, infrastructure, banking, or social media. What exactly does it do for us and what does a machine learning specialist do? Machine learning professionals create and implement special algorithms that can learn from existing data to make an accurate prediction on new never before seen data. Python Machine Learning presents you a step-by-step guide on how to create machine learning models that lead to valuable results. The book focuses on machine learning theory as much as practical examples. You will learn how to analyse data, use visualization methods, implement regression and classification models, and how to harness the power of neural networks. By purchasing this book, your machine learning journey becomes a lot easier. While a minimal level of Python programming is recommended, the algorithms and techniques are explained in such a way that you don’t need to be intimidated by mathematics. The Topics Covered Include: Machine learning fundamentals How to set up the development environment How to use Python libraries and modules like Scikit-learn, TensorFlow, Matplotlib, and NumPy How to explore data How to solve regression and classification problems Decision trees k-means clustering Feed-forward and recurrent neural networks Get your copy now
Python Programming and Machine Learning by John S Code Pdf
Are you new to machine learning? Do you want to learn how to do machine learning with Python? Have you been thinking of learning Python as your first programming language?Artificial intelligent, Data analysis, Coding languages are subjects you need to start a super career today. The use of machine learning offers incredible opportunities!This ultimate book will give you the opportunity to understand coding languages and analysing big data to help the decision makers into meaningful information.Why with Python? Because Python is a powerful interpreted language and the best programming language to start with.Python is a complete language and platform where you can apply both research and development production. This book includes: Python Programming for Beginners This book can be your easy guide to understand coding language, Python programming, and data analysis with tricks and tools. It comes with 11 chapters that will teach you about python programming. Python Machine Learning It can be your essential book to know about artificial intelligence, neural network, mastering, and deep learning about the fundamentals of ML with Python. It consists of 12 chapters that will help you hone your skills and knowledge about machine learning. Improve your coding skills starting with an easy guide and master the fundamentals of machine learning with Python. You do not need any experience to change your career, just learn this book. So, what are you waiting for? Purchase yours today!
Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
Fundamentals of Machine & Deep Learning by V K Sachan Pdf
This book is written to provide a solid foundation in machine learning using Python libraries by providing case studies and real-life examples. It covers topics such as the fundamentals of machine learning, the introduction to Python. It is intended for students studying machine learning in their Undergraduate and Post graduate courses in Information Technology, Computer Engineering, Electronics and Communications Engineering and Electrical Engineering. An introduction to a wide range of deep learning topics, covering conceptual and mathematical backgrounds, deep learning techniques used in industry, and research perspectives. The text provides conceptual and mathematical background, covering relevant concepts in linear algebra, probability theory, and information theory, number computing, and machine learning. It describes the deep learning techniques used by industry professionals, including deep feedback networks, regularization, optimization algorithms, convolution networks, and hands-on methodology. This text book "Fundamentals of Machine and Deep Learning" is organized into Ten Chapters. Chapter -1: Machine LearningChapter-2: Machine Learning AlgorithmsChapter -3: Models of Machine LearningChapter-4: Artificial Intelligence and Machine LearningChapter 5: Mathematics & Python Programs for Machine LearningChapter- 6: Future Scope of Machine learning in FutureChapter 7: Introduction of Deep LearningChapter-8: Models for Deep LearningChapter 9: Deep Learning AlgorithmsChapter- 10: Future Scope of Deep LearningSalient Features Comprehensive Coverage of Machine Learning Algorithms, Models of Machine Learning, Artificial Intelligence and Machine Learning, Mathematics & Python Programs for Machine Learning and Future Scope of Machine learning in Future New elements in book include Deep Learning, Models for Deep Learning, Deep Learning Algorithms and Future Scope of Deep Learning. Clear perception of the various program using Python with a large number of neat, well drawn and illustrative diagrams. Simple Language, easy- to- understand manner.Our sincere thanks are due to all Scientists, Engineers, Authors and Publishers, whose works and text have been the source of enlightenment, inspiration and guidance to us in presenting this small book. I will appreciate any suggestions from students and faculty members alike so that we can strive to make the text book more useful in the edition to come.
Machine Learning with Python by Abhishek Vijayvargia Pdf
Providing code examples in python, this book introduces the concepts of machine learning with mathematical explanations and programming fundamentals. --