Hands-On Unsupervised Learning Using Python PDF Download

Author: Ankur A. Patel
Publisher: "O'Reilly Media, Inc."
ISBN: 1492035599
Category : Computers
Languages : en
Pages : 310

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Book Description
Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks

Author: Taweh Beysolow II
Publisher: Apress
ISBN: 148425127X
Category : Computers
Languages : en
Pages : 177

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Book Description
Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions. What You'll Learn Implement reinforcement learning with Python Work with AI frameworks such as OpenAI Gym, Tensorflow, and KerasDeploy and train reinforcement learning–based solutions via cloud resourcesApply practical applications of reinforcement learning Who This Book Is For Data scientists, machine learning engineers and software engineers familiar with machine learning and deep learning concepts.

Author: Siddhanta Bhatta
Publisher: BPB Publications
ISBN: 9391030432
Category : Computers
Languages : en
Pages : 418

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Book Description
A problem-focused guide for tackling industrial machine learning issues with methods and frameworks chosen by experts. KEY FEATURES ● Popular techniques for problem formulation, data collection, and data cleaning in machine learning. ● Comprehensive and useful machine learning tools such as MLFlow, Streamlit, and many more. ● Covers numerous machine learning libraries, including Tensorflow, FastAI, Scikit-Learn, Pandas, and Numpy. DESCRIPTION This book discusses how to apply machine learning to real-world problems by utilizing real-world data. In this book, you will investigate data sources, become acquainted with data pipelines, and practice how machine learning works through numerous examples and case studies. The book begins with high-level concepts and implementation (with code!) and progresses towards the real-world of ML systems. It briefly discusses various concepts of Statistics and Linear Algebra. You will learn how to formulate a problem, collect data, build a model, and tune it. You will learn about use cases for data analytics, computer vision, and natural language processing. You will also explore nonlinear architecture, thus enabling you to build models with multiple inputs and outputs. You will get trained on creating a machine learning profile, various machine learning libraries, Statistics, and FAST API. Throughout the book, you will use Python to experiment with machine learning libraries such as Tensorflow, Scikit-learn, Spacy, and FastAI. The book will help train our models on both Kaggle and our datasets. WHAT YOU WILL LEARN ● Construct a machine learning problem, evaluate the feasibility, and gather and clean data. ● Learn to explore data first, select, and train machine learning models. ● Fine-tune the chosen model, deploy, and monitor it in production. ● Discover popular models for data analytics, computer vision, and Natural Language Processing. ● Create a machine learning profile and contribute to the community. WHO THIS BOOK IS FOR This book caters to beginners in machine learning, software engineers, and students who want to gain a good understanding of machine learning concepts and create production-ready ML systems. This book assumes you have a beginner-level understanding of Python. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Problem Formulation in Machine Learning 3. Data Acquisition and Cleaning 4. Exploratory Data Analysis 5. Model Building and Tuning 6. Taking Our Model into Production 7. Data Analytics Use Case 8. Building a Custom Image Classifier from Scratch 9. Building a News Summarization App Using Transformers 10. Multiple Inputs and Multiple Output Models 11. Contributing to the Community 12. Creating Your Project 13. Crash Course in Numpy, Matplotlib, and Pandas 14. Crash Course in Linear Algebra and Statistics 15. Crash Course in FastAPI

Author: William Gray
Publisher: Independently Published
ISBN: 9781096755364
Category :
Languages : en
Pages : 276

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Book Description
What exactly is machine learning and why is it so valuable in the online business ? Are you thinking of learning Python machine learning ?This book teach well you the practical ways to do it ! ★★★ Buy the Paperback version and get the Kindle Book versions for FREE ★★★ Machine Learning is a branch of AI that applied algorithms to learn from data and create predictions - this is important in predicting the world around us. Python is a popular and open-source programming language. In addition, it is one of the most applied languages in artificial intelligence and other scientific fields. Today, it is a top skill in high demand in the job market. Machine learning has become an integral part of many commercial applications and research projects. Using Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. Inside Introduction to Machine Learning with Python, you'll learn: Fundamental concepts and applications of machine learning Understand the various categories of machine learning algorithms. Some of the branches of Artificial Intelligence The basics of Python Concepts of Machine Learning using Python Python Machine Learning Applications Machine Learning Case Studies with Python The way that Python evolved throughout time And many more Throughout the recent years, artificial intelligence and machine learning have made some enormous, significant strides in terms of universal, global applicability. You'll discover the steps required to develop a successful machine-learning application using Python. Introduction to Machine Learning with Python is a step-by-step guide for any person who wants to start learning Artificial Intelligence - It will help you in preparing a solid foundation and learn any other high-level courses. Stay ahead and make a choice that will last... If You like to know more, scroll to the top and select " BUY NOW " buttom ★★★ Buy the Paperback version and get the Kindle Book versions for FREE ★★★

Author: Chris Albon
Publisher: "O'Reilly Media, Inc."
ISBN: 1491989335
Category : Computers
Languages : en
Pages : 305

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Book Description
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models

Author: Sebastian Raschka
Publisher: Packt Publishing Ltd
ISBN: 1783555149
Category : Computers
Languages : en
Pages : 455

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Book Description
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.

Author: Andreas C. Müller
Publisher: "O'Reilly Media, Inc."
ISBN: 1449369898
Category : Computers
Languages : en
Pages : 429

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Book Description
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

Author: Andrea Giussani
Publisher:
ISBN: 9788899902827
Category : Computers
Languages : en
Pages : 182

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Book Description

Author: Dipanjan Sarkar
Publisher: Apress
ISBN: 1484232070
Category : Computers
Languages : en
Pages : 545

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Book Description
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students

Author: Benjamin Bengfort
Publisher: "O'Reilly Media, Inc."
ISBN: 1491962992
Category : Computers
Languages : en
Pages : 328

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Book Description
From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity