Paperback A Practical Guide To Implementing Supervised And Unsupervised Machine Learning Algorithms In Python PDF Download

Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Paperback A Practical Guide To Implementing Supervised And Unsupervised Machine Learning Algorithms In Python PDF full book. Access full book title Paperback A Practical Guide To Implementing Supervised And Unsupervised Machine Learning Algorithms In Python.

Paperback - a Practical Guide to Implementing Supervised and Unsupervised Machine Learning Algorithms in Python

Paperback - a Practical Guide to Implementing Supervised and Unsupervised Machine Learning Algorithms in Python
Author: Terry FISHER
Publisher:
Total Pages: 351
Release: 2021-03
Genre:
ISBN:
GET BOOK

Download Paperback - a Practical Guide to Implementing Supervised and Unsupervised Machine Learning Algorithms in Python Book in PDF, ePub and Kindle

What you will learn Understand when to use supervised, unsupervised, or reinforcement learning algorithms Find out how to collect and prepare your data for machine learning tasks Tackle imbalanced data and optimize your algorithm for a bias or variance tradeoff Apply supervised and unsupervised algorithms to overcome various machine learning challenges Employ best practices for tuning your algorithm's hyper parameters Discover how to use neural networks for classification and regression Build, evaluate, and deploy your machine learning solutions to production Who this book is for This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.

Python Machine Learning

Python Machine Learning
Author: François Duval
Publisher: Createspace Independent Publishing Platform
Total Pages: 136
Release: 2017-02-17
Genre:
ISBN: 9781985670969
GET BOOK

Download Python Machine Learning Book in PDF, ePub and Kindle

****Buy now (Will soon return to $35.99 + Special Offer Below) **** Free Kindle eBook for customers who purchase the print book Are you thinking of learning more about Machine Learning with Practical Examples using Python? Machine learning is a field of Artificial Intelligence that uses algorithms to learn from data and make predictions. This means that we can feed data into an algorithm, and use it to make predictions about what might happen in the future. If you are looking for a book to help you understand how the Machine learning works by using Python, then this is a good book for you. Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images which detail all algorithms and their applications in all area of the real life. Why this book is different? This book takes a different approach that is based on providing simple examples of how machine learning algorithms work, and building on those examples step by step to encompass the more complicated parts of the algorithms. The book is a practical guide through the basic principles of machine learning, and how to get started with machine learning using Python based on libraries that make it easy to start. Python Codes for the Examples Shown In the Book You will build your machine learning model by using Python Target Users The book designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach machine learning practices, but are too afraid to start Newbies in computer science techniques and machine learning Professionals in data science and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on machine learning and deep learning What's Inside this Book? Introduction to Machine Learning? Essential Libraries and their Installation Basic of Python Language in Machine Learning Data and Inconsistencies in Machine Learning A Roadmap for building Machine Learning Systems Data Cleaning and Preparation Application of Supervised Learning Techniques with Python Applications of unsupervised learning Techniques with python Training Machine Learning Algorithms Combining Different Models for ensemble learning Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: If you want to smash machine learning problems with Python and TensorFlow, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a data science expert? A: Unfortunately, no. This book is designed for readers taking their first steps in machine learning and further learning will be required beyond this book to master all aspects of machine learning. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. will also be happy to help you if you send us an email at [email protected].

Supervised Machine Learning with Python

Supervised Machine Learning with Python
Author: Taylor Smith
Publisher: Packt Publishing Ltd
Total Pages: 156
Release: 2019-05-27
Genre: Computers
ISBN: 1838823069
GET BOOK

Download Supervised Machine Learning with Python Book in PDF, ePub and Kindle

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.

Machine Learning from Scratch

Machine Learning from Scratch
Author: Alain Kaufmann
Publisher: Createspace Independent Publishing Platform
Total Pages: 120
Release: 2017-03-06
Genre:
ISBN: 9781986999571
GET BOOK

Download Machine Learning from Scratch Book in PDF, ePub and Kindle

***** Buy now (Will soon return to $38.95 + Special Offer Below) ***** Free Kindle eBook for customers who purchase the print book from Amazon Are you thinking of learning more about Machine Learning From Scratch by using Python? The overall aim of this book is to give you an application of machine learning techniques with python. Machine learning is a field of Artificial Intelligence that uses algorithms to learn from data and make predictions. This means that we can feed data into an algorithm, and use it to make predictions about what might happen in the future. This book is a practical guide through the basic principles of machine learning, and how to get started with machine learning using Python based on libraries that make machine learning easy to get started with. Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images, which detail all-important Machine learning concepts and their applications. This Is a Practical Guide Book This book will help you explore exactly the most important machine learning techniques by using python and real data. It is a step-by-step book. You will build our Machine Learning Models by using Python Target Users The book designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach data science, but are too afraid of complex math to start Newbies in computer science techniques and machine learning Professionals in data science and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on data science What's Inside This Great Book? Introduction Using Python for Machine Learning Steps to Solving Machine Learning Problems A Machine Learning Example: Predicting Housing Prices Here's Where Real Machine Learning Starts What If Regression Doesn't Apply? How to Improve Your Model's Performance How to Improve Your Model's Performance Neural Networks & Deep Learning The Future of Machine Learning Glossary on Important Machine Learning Terms Sources & References Bonus Chapter: Anaconda Setup & Python Crash Course Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: f you want to smash Data Science from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a data science expert? A: Unfortunately, no. This book is designed for readers taking their first steps in data science and further learning will be required beyond this book to master all aspects of data science. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. will also be happy to help you if you send us an email at [email protected].

Pro Machine Learning Algorithms

Pro Machine Learning Algorithms
Author: V Kishore Ayyadevara
Publisher: Apress
Total Pages: 379
Release: 2018-06-30
Genre: Computers
ISBN: 1484235649
GET BOOK

Download Pro Machine Learning Algorithms Book in PDF, ePub and Kindle

Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. What You Will Learn Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning Who This Book Is For Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.

Machine Learning With Python

Machine Learning With Python
Author: Rosalba Wable
Publisher: Independently Published
Total Pages: 124
Release: 2021-02-20
Genre:
ISBN:
GET BOOK

Download Machine Learning With Python Book in PDF, ePub and Kindle

Machine Learning (ML) is basically that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do. In simple words, ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method. In this step-by-step guide you will learn: - To code machine learning models using a range of supervised learning algorithms including logistic regression, gradient boosting, and decision trees - Clean and inspect your data using free machine learning libraries - Visualize relationships in your dataset including Heatmaps and Pairplots with just a few lines of code - Develop your expertise in managing data using Python

Python Machine Learning

Python Machine Learning
Author: Railey Brandon
Publisher: Roland Bind
Total Pages: 152
Release: 2019-04-25
Genre: Computers
ISBN:
GET BOOK

Download Python Machine Learning Book in PDF, ePub and Kindle

★☆Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes?☆★ If you responded yes to any of the above questions, you have come to the right place. Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that. Machine learning and artificial intelligence have been used in different machines and applications to improve the user's experience. One can also use machine learning to make data analysis and predicting the output for some data sets easy. All you need to do is choose the right algorithm, train the model and test the model before you apply it on any real-world tool. It is that simple isn't it? ★★Apart from this, you will also learn more about★★ ♦ The Different Types Of Learning Algorithm That You Can Expect To Encounter ♦ The Numerous Applications Of Machine Learning And Deep Learning ♦ The Best Practices For Picking Up Neural Networks ♦ What Are The Best Languages And Libraries To Work With ♦ The Various Problems That You Can Solve With Machine Learning Algorithms ♦ And much more... Well, you can do it faster if you use Python. This language has made it easy for any user, even an amateur, to build a strong machine learning model since it has numerous directories and libraries that make it easy for one to build a model. Do you want to know how to build a machine learning model and a neural network? So, what are you waiting for? Grab a copy of this book now!

Practical Machine Learning with Python

Practical Machine Learning with Python
Author: Dipanjan Sarkar
Publisher: Apress
Total Pages: 545
Release: 2017-12-20
Genre: Computers
ISBN: 1484232070
GET BOOK

Download Practical Machine Learning with Python Book in PDF, ePub and Kindle

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

Python Machine Learning

Python Machine Learning
Author: William Gray
Publisher: Independently Published
Total Pages: 240
Release: 2019-05-31
Genre:
ISBN: 9781070792316
GET BOOK

Download Python Machine Learning Book in PDF, ePub and Kindle

What do you know about Python, are you afraid it's not enough ? Moving to a higher level is your next goal ? ★★★ Buy the Paperback version and get the Kindle Book versions for FREE ★★★ Machine learning is changing every aspect of our lives !!! Today, ML algorithms accomplish tasks that until recently only expert humans could perform and, as machines get ever more complex and perform more and more tasks to free up our time, so it is that new ideas are developed to help us continually improve their speed and abilities. Programmers who know close to nothing about this technology, now, can use simple, efficient tools to implement programs capable of learning from data. In this practical guide, you will discover: Understand the key frameworks in ML Latest Python open source libraries in ML ML techniques using real-world data The ML Classifiers Using Scikit-Learn Implementing a Multilayer Artificial Neural Network from Scratch The Mechanics of TensorFlow ML Model into a Web Application The future of ML You are required to have installed the following on your computer: Python 3.X Numpy Pandas Matplotlib This book offers a lot of insight into machine learning for both beginners, as well as for professionals, who already use some machine learning techniques. Using the latest Python open source libraries, this book offers the practical knowledge you need to create and contribute to machine learning and modern data analysis. Get a copy, of Python Machine Learning, today and see where the future lies ! By scrolling to the top of the page, select " Add to Cart " button ★★★ Buy the Paperback version and get the Kindle Book versions for FREE ★★★