Supervised Machine Learning With Python

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Supervised Machine Learning with Python

Author : Taylor Smith
Publisher : Packt Publishing Ltd
ISBN 13 : 1838823069
Total Pages : 156 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Supervised Machine Learning with Python by : Taylor Smith

Download or read book Supervised Machine Learning with Python written by Taylor Smith and published by Packt Publishing Ltd. This book was released on 2019-05-27 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Teach your machine to think for itself! Key FeaturesDelve into supervised learning and grasp how a machine learns from dataImplement popular machine learning algorithms from scratch, developing a deep understanding along the wayExplore some of the most popular scientific and mathematical libraries in the Python languageBook Description Supervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine “learns” under the hood. This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems. What you will learnCrack how a machine learns a concept and generalize its understanding to new dataUncover the fundamental differences between parametric and non-parametric modelsImplement and grok several well-known supervised learning algorithms from scratchWork with models in domains such as ecommerce and marketingExpand your expertise and use various algorithms such as regression, decision trees, and clusteringBuild your own models capable of making predictionsDelve into the most popular approaches in deep learning such as transfer learning and neural networksWho this book is for This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming—and some fundamental knowledge of supervised learning—are expected.

Supervised Learning with Python

Author : Vaibhav Verdhan
Publisher : Apress
ISBN 13 : 9781484261552
Total Pages : 372 pages
Book Rating : 4.2/5 (615 download)

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Book Synopsis Supervised Learning with Python by : Vaibhav Verdhan

Download or read book Supervised Learning with Python written by Vaibhav Verdhan and published by Apress. This book was released on 2020-10-08 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as text and images datasets. You’ll start with an introduction to machine learning, highlighting the differences between supervised, semi-supervised and unsupervised learning. In the following chapters you’ll study regression and classification problems, mathematics behind them, algorithms like Linear Regression, Logistic Regression, Decision Tree, KNN, Naïve Bayes, and advanced algorithms like Random Forest, SVM, Gradient Boosting and Neural Networks. Python implementation is provided for all the algorithms. You’ll conclude with an end-to-end model development process including deployment and maintenance of the model. After reading Supervised Learning with Python you’ll have a broad understanding of supervised learning and its practical implementation, and be able to run the code and extend it in an innovative manner. What You'll Learn Review the fundamental building blocks and concepts of supervised learning using Python Develop supervised learning solutions for structured data as well as text and images Solve issues around overfitting, feature engineering, data cleansing, and cross-validation for building best fit models Understand the end-to-end model cycle from business problem definition to model deployment and model maintenance Avoid the common pitfalls and adhere to best practices while creating a supervised learning model using Python Who This Book Is For Data scientists or data analysts interested in best practices and standards for supervised learning, and using classification algorithms and regression techniques to develop predictive models.

Applied Supervised Learning with Python

Author : Benjamin Johnston
Publisher : Packt Publishing Ltd
ISBN 13 : 1789955831
Total Pages : 404 pages
Book Rating : 4.7/5 (899 download)

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Book Synopsis Applied Supervised Learning with Python by : Benjamin Johnston

Download or read book Applied Supervised Learning with Python written by Benjamin Johnston and published by Packt Publishing Ltd. This book was released on 2019-04-27 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the exciting world of machine learning with the fastest growing technology in the world Key FeaturesUnderstand various machine learning concepts with real-world examplesImplement a supervised machine learning pipeline from data ingestion to validationGain insights into how you can use machine learning in everyday lifeBook Description Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own! What you will learnUnderstand the concept of supervised learning and its applicationsImplement common supervised learning algorithms using machine learning Python librariesValidate models using the k-fold techniqueBuild your models with decision trees to get results effortlesslyUse ensemble modeling techniques to improve the performance of your modelApply a variety of metrics to compare machine learning modelsWho this book is for Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.

Hands-on Supervised Learning with Python

Author : Gnana Lakshmi T C
Publisher : BPB Publications
ISBN 13 : 9389328977
Total Pages : 382 pages
Book Rating : 4.3/5 (893 download)

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Book Synopsis Hands-on Supervised Learning with Python by : Gnana Lakshmi T C

Download or read book Hands-on Supervised Learning with Python written by Gnana Lakshmi T C and published by BPB Publications. This book was released on 2021-01-06 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-On ML problem solving and creating solutions using Python KEY FEATURES _Introduction to Python Programming _Python for Machine Learning _Introduction to Machine Learning _Introduction to Predictive Modelling, Supervised and Unsupervised Algorithms _Linear Regression, Logistic Regression and Support Vector MachinesÊ DESCRIPTIONÊ You will learn about the fundamentals of Machine Learning and Python programming post, which you will be introduced to predictive modelling and the different methodologies in predictive modelling. You will be introduced to Supervised Learning algorithms and Unsupervised Learning algorithms and the difference between them.Ê We will focus on learning supervised machine learning algorithms covering Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees and Artificial Neural Networks. For each of these algorithms, you will work hands-on with open-source datasets and use python programming to program the machine learning algorithms. You will learn about cleaning the data and optimizing the features to get the best results out of your machine learning model. You will learn about the various parameters that determine the accuracy of your model and how you can tune your model based on the reflection of these parameters. WHAT WILL YOU LEARN _Get a clear vision of what is Machine Learning and get familiar with the foundation principles of Machine learning. _Understand the Python language-specific libraries available for Machine learning and be able to work with those libraries. _Explore the different Supervised Learning based algorithms in Machine Learning and know how to implement them when a real-time use case is presented to you. _Have hands-on with Data Exploration, Data Cleaning, Data Preprocessing and Model implementation. _Get to know the basics of Deep Learning and some interesting algorithms in this space. _Choose the right model based on your problem statement and work with EDA techniques to get good accuracy on your model WHO THIS BOOK IS FOR This book is for anyone interested in understanding Machine Learning. Beginners, Machine Learning Engineers and Data Scientists who want to get familiar with Supervised Learning algorithms will find this book helpful. TABLE OF CONTENTS Ê1. ÊIntroduction to Python Programming Ê2. Python for Machine LearningÊÊÊÊÊ Ê3.Ê Introduction to Machine LearningÊÊÊÊÊÊÊÊÊ Ê4. Supervised Learning and Unsupervised LearningÊÊÊÊÊÊÊÊÊ Ê5. Linear Regression: A Hands-on guideÊÊÊ Ê6. Logistic Regression Ð An Introduction Ê7. A sneak peek into the working of Support Vector machines(SVM)ÊÊÊÊÊÊ Ê8. Decision Trees Ê9. Random Forests Ê10. ÊTime Series models in Machine Learning Ê11.Ê Introduction to Neural Networks Ê12. ÊÊÊRecurrent Neural Networks Ê13. ÊÊÊConvolutional Neural Networks Ê14. ÊÊÊPerformance Metrics Ê15. ÊÊÊIntroduction to Design Thinking Ê16. Ê Design Thinking Case Study

Machine Learning and Data Science Blueprints for Finance

Author : Hariom Tatsat
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492073008
Total Pages : 432 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Machine Learning and Data Science Blueprints for Finance by : Hariom Tatsat

Download or read book Machine Learning and Data Science Blueprints for Finance written by Hariom Tatsat and published by "O'Reilly Media, Inc.". This book was released on 2020-10-01 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Hands-On Unsupervised Learning Using Python

Author : Ankur A. Patel
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492035599
Total Pages : 310 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Hands-On Unsupervised Learning Using Python by : Ankur A. Patel

Download or read book Hands-On Unsupervised Learning Using Python written by Ankur A. Patel and published by "O'Reilly Media, Inc.". This book was released on 2019-02-21 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Python Machine Learning

Author : Wei-Meng Lee
Publisher : John Wiley & Sons
ISBN 13 : 1119545692
Total Pages : 324 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Python Machine Learning by : Wei-Meng Lee

Download or read book Python Machine Learning written by Wei-Meng Lee and published by John Wiley & Sons. This book was released on 2019-04-04 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart—it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand. • Python data science—manipulating data and data visualization • Data cleansing • Understanding Machine learning algorithms • Supervised learning algorithms • Unsupervised learning algorithms • Deploying machine learning models Python Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level.

Hands-On Machine Learning with Scikit-learn and Scientific Python Toolkits

Author : Tarek Amr
Publisher :
ISBN 13 : 9781838826048
Total Pages : 384 pages
Book Rating : 4.8/5 (26 download)

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Book Synopsis Hands-On Machine Learning with Scikit-learn and Scientific Python Toolkits by : Tarek Amr

Download or read book Hands-On Machine Learning with Scikit-learn and Scientific Python Toolkits written by Tarek Amr and published by . This book was released on 2020-07-24 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Programming Machine Learning

Author : Paolo Perrotta
Publisher : Pragmatic Bookshelf
ISBN 13 : 1680507710
Total Pages : 437 pages
Book Rating : 4.6/5 (85 download)

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Book Synopsis Programming Machine Learning by : Paolo Perrotta

Download or read book Programming Machine Learning written by Paolo Perrotta and published by Pragmatic Bookshelf. This book was released on 2020-03-31 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.

Introduction to Machine Learning with Python

Author : Andreas C. Müller
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1449369898
Total Pages : 429 pages
Book Rating : 4.4/5 (493 download)

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Book Synopsis Introduction to Machine Learning with Python by : Andreas C. Müller

Download or read book Introduction to Machine Learning with Python written by Andreas C. Müller and published by "O'Reilly Media, Inc.". This book was released on 2016-09-26 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Machine Learning for Kids

Author : Dale Lane
Publisher : No Starch Press
ISBN 13 : 1718500572
Total Pages : 290 pages
Book Rating : 4.7/5 (185 download)

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Book Synopsis Machine Learning for Kids by : Dale Lane

Download or read book Machine Learning for Kids written by Dale Lane and published by No Starch Press. This book was released on 2021-01-19 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based, award-winning companion website, you'll see how easy it is to add machine learning to your own projects. You don't even need to know how to code! As you work through the book you'll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve their accuracy. You'll turn your models into fun computer games and apps, and see what happens when they get confused by bad data. You'll build 13 projects step-by-step from the ground up, including: • Rock, Paper, Scissors game that recognizes your hand shapes • An app that recommends movies based on other movies that you like • A computer character that reacts to insults and compliments • An interactive virtual assistant (like Siri or Alexa) that obeys commands • An AI version of Pac-Man, with a smart character that knows how to avoid ghosts NOTE: This book includes a Scratch tutorial for beginners, and step-by-step instructions for every project. Ages 12+

Statistics for Machine Learning

Author : Pratap Dangeti
Publisher : Packt Publishing Ltd
ISBN 13 : 1788291220
Total Pages : 438 pages
Book Rating : 4.7/5 (882 download)

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Book Synopsis Statistics for Machine Learning by : Pratap Dangeti

Download or read book Statistics for Machine Learning written by Pratap Dangeti and published by Packt Publishing Ltd. This book was released on 2017-07-21 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn Understand the Statistical and Machine Learning fundamentals necessary to build models Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages Analyze the results and tune the model appropriately to your own predictive goals Understand the concepts of required statistics for Machine Learning Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.

Artificial Intelligence with Python

Author : Prateek Joshi
Publisher : Packt Publishing Ltd
ISBN 13 : 1786469677
Total Pages : 437 pages
Book Rating : 4.7/5 (864 download)

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Book Synopsis Artificial Intelligence with Python by : Prateek Joshi

Download or read book Artificial Intelligence with Python written by Prateek Joshi and published by Packt Publishing Ltd. This book was released on 2017-01-27 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.

Practical Machine Learning for Data Analysis Using Python

Author : Abdulhamit Subasi
Publisher : Academic Press
ISBN 13 : 0128213809
Total Pages : 536 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Practical Machine Learning for Data Analysis Using Python by : Abdulhamit Subasi

Download or read book Practical Machine Learning for Data Analysis Using Python written by Abdulhamit Subasi and published by Academic Press. This book was released on 2020-06-05 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. - Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas - Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data - Explores important classification and regression algorithms as well as other machine learning techniques - Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features

Machine Learning Algorithms Using Python Programming

Author : Gopal Sakarkar
Publisher : Nova Science Publishers
ISBN 13 : 9781536196153
Total Pages : 182 pages
Book Rating : 4.1/5 (961 download)

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Book Synopsis Machine Learning Algorithms Using Python Programming by : Gopal Sakarkar

Download or read book Machine Learning Algorithms Using Python Programming written by Gopal Sakarkar and published by Nova Science Publishers. This book was released on 2021 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The machine learning field is concerned with the question of how to create computer programs that automatically improve information. In recent years, many successful electronic learning applications have been made, from data mining systems that learn to detect fraudulent credit card transactions, filtering programs that learn user readings, to private cars that learn to drive on public highways. At the same time, there have been significant developments in the concepts and algorithms that form the basis for this field. Machine learning is programming computers to optimize a performance criterion using example data or past experience. The goal of this textbook is to present the key concepts of Machine Learning which includes Python concepts and Interpreter, Foundation of Machine Learning, Data Pre-processing, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning, Kernel Machine, Design and analysis of Machine Learning experiment and Data visualization. The theoretical concepts along with coding implementation are covered. This book aims to pursue a middle ground between a theoretical textbook and one that focuses on applications. The book concentrates on the important ideas in machine learning"--

Practical Machine Learning for Streaming Data with Python

Author : Sayan Putatunda
Publisher : Apress
ISBN 13 : 9781484268667
Total Pages : 118 pages
Book Rating : 4.2/5 (686 download)

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Book Synopsis Practical Machine Learning for Streaming Data with Python by : Sayan Putatunda

Download or read book Practical Machine Learning for Streaming Data with Python written by Sayan Putatunda and published by Apress. This book was released on 2021-04-09 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You'll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow. Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. The book concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more. What You'll Learn Understand machine learning with streaming data concepts Review incremental and online learning Develop models for detecting concept drift Explore techniques for classification, regression, and ensemble learning in streaming data contexts Apply best practices for debugging and validating machine learning models in streaming data context Get introduced to other open-source frameworks for handling streaming data. Who This Book Is For Machine learning engineers and data science professionals

Hands-On Quantum Machine Learning With Python

Author : Frank Zickert
Publisher : Independently Published
ISBN 13 :
Total Pages : 440 pages
Book Rating : 4.5/5 (165 download)

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Book Synopsis Hands-On Quantum Machine Learning With Python by : Frank Zickert

Download or read book Hands-On Quantum Machine Learning With Python written by Frank Zickert and published by Independently Published. This book was released on 2021-06-19 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: You're interested in quantum computing and machine learning. But you don't know how to get started? Let me help! Whether you just get started with quantum computing and machine learning or you're already a senior machine learning engineer, Hands-On Quantum Machine Learning With Python is your comprehensive guide to get started with Quantum Machine Learning - the use of quantum computing for the computation of machine learning algorithms. Quantum computing promises to solve problems intractable with current computing technologies. But is it fundamentally different and asks us to change the way we think. Hands-On Quantum Machine Learning With Python strives to be the perfect balance between theory taught in a textbook and the actual hands-on knowledge you'll need to implement real-world solutions. Inside this book, you will learn the basics of quantum computing and machine learning in a practical and applied manner.