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Data Analytics for Absolute Beginners: a Deconstructed Guide to Data Literacy

Author : Oliver Theobald

Publisher :

Page : 88 pages

File Size : 44,20 MB

Release : 2019-07-21

Category :

ISBN : 9781081762469

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Book Description

While exposure to data has become more or less a daily ritual for the rank-and-file knowledge worker, true understanding-treated in this book as data literacy-resides in knowing what lies behind the data. Everything from the data's source to the specific choice of input variables, algorithmic transformations, and visual representation shape the accuracy, relevance, and value of the data and mark its journey from raw data to business insight. It's also important to grasp the terminology and basic concepts of data analytics as much as it is to have the financial literacy to be successful as a decisionmaker in the business world. In this book, we make sense of data analytics without the assumption that you understand specific data science terminology or advanced programming languages to set you on your path. Topics covered in this book: Data Mining Big Data Machine Learning Alternative Data Data Management Web Scraping Regression Analysis Clustering Analysis Association Analysis Data Visualization Business Intelligence



Machine Learning For Dummies

Author : John Paul Mueller

Publisher : John Wiley & Sons

Page : 471 pages

File Size : 20,60 MB

Release : 2021-02-09

Category : Computers

ISBN : 1119724015

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Book Description

One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more. Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learning Work with Python 3.8 and TensorFlow 2.x (and R as a download) Build and test your own models Use the latest datasets, rather than the worn out data found in other books Apply machine learning to real problems Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.



Machine Learning: Make Your Own Recommender System

Author : Oliver Theobald

Publisher : Machine Learning for Beginners

Page : 120 pages

File Size : 11,50 MB

Release : 2018-10-06

Category : Computers

ISBN : 9781726769037

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Book Description

Learn How to Make Your Own Recommender System in an Afternoon.Recommender systems are one of the most visible applications of machine learning and data mining today and their uncanny ability to convert our unspoken actions into items we desire is both addicting and concerning. And whether recommender systems excite or scare you, the best way to manage their influence and impact is to understand the architecture and algorithms that play on your personal data. Recommender systems are here to stay and for anyone beginning their journey in data science, this is a lucrative space for future employment.This book will get you up and running with the basics as well as the steps to coding your own recommender system. Exercises include predicting book recommendations, relevant house properties for online marketing purposes, and whether a user will click on an ad campaign. The contents of this book is designed for beginners with some background knowledge of data science, including classical statistics and computing programming. If this is your first exposure to data science, you may want to spend a few hours to read my first book Machine Learning for Absolute Beginners before you get started here.Topics covered in this book: Setting Up A Sandbox Environment With Jupyter NotebookWorking With DataData ReductionBuilding a Collaborative Filtering ModelBuilding a Content-Based Filtering ModelEvaluationPrivacy & EthicsFuture of Recommender SystemsPlease feel welcome to join this introductory course by buying a copy or sending a free sample to your preferred device.



Machine Learning for Beginners

Author : Chris Sebastian

Publisher : Python, Machine Learning

Page : 164 pages

File Size : 33,25 MB

Release : 2019

Category : Computers

ISBN : 9781793016423

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Book Description

♦♦Bonus: Buy the Paperback version of this book, and get the kindle eBook version included for FREE** Machine Learning is changing the world. You use Machine Learning every day and probably don't know it. In this book, you will learn how ML grew from a desire to make computers able to learn. Trace the development of Machine Learning from the early days of a computer learning how to play checkers, to machines able to beat world masters in chess and go. Understand how large data is so important to Machine Learning, and how the collection of massive amounts of data provides Machine Learning programmers with the information they need to developing learning algorithms.Simple examples will help you understand the complex math and probability statistics underlining Machine Learning. You will also see real-world examples of Machine Learning in action and uncover how these algorithms are making your life better every day.Learn about how artificial intelligence, Machine Learning, Neural Networks, and Swarm Intelligence interact and complement each other as part of the quest to generate machines capable of thinking and reacting to the world. Read about the technical issues with Machine Learning and how they are being overcome. Discover the dark side of ML and what possible outcomes there could be should things go wrong. And finally, learn about the positive future artificial intelligence and Machine Learning promise to bring to the world. In this book, you will discover *The history of Machine Learning *Approaches taken to ML in the past and present *Artificial intelligence and its relationship to ML *How neural networks, big data, regression, and the cloud all play a part in the development of Machine Learning *Compare Machine Learning to the Internet of Things, Robotics, and Swarm Intelligence *Learn about the different models of ML and how each is used to produce learning algorithms *Get access to free software and data sets so you can try out your very own Machine Learning software *Examine some of the technical problems and philosophical dilemmas with ML *See what advanced Machine Learning will make to our world in the future So what are you waiting for???Scroll back up and order this book NOW.



Summary of Oliver Theobald's Machine Learning for Absolute Beginners

Author : Milkyway Media

Publisher : Milkyway Media

Page : 16 pages

File Size : 12,75 MB

Release : 2024-03-26

Category : Mathematics

ISBN :

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Book Description

Get the Summary of Oliver Theobald's Machine Learning for Absolute Beginners in 20 minutes. Please note: This is a summary & not the original book. "Machine Learning for Absolute Beginners" by Oliver Theobald provides an introduction to the expansive field of data science, which includes big data, AI, data mining, and machine learning. The book traces the evolution of data analysis from classical statistics, which could be managed manually, to the complex, computer-dependent techniques of today. It explains how AI aims to replicate human cognitive abilities and how machine learning, a subset of AI, uses algorithms to learn from data and make predictions...



AI and Machine Learning for Coders

Author : Laurence Moroney

Publisher : O'Reilly Media

Page : 393 pages

File Size : 10,53 MB

Release : 2020-10-01

Category : Computers

ISBN : 1492078166

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Book Description

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving



Machine Learning with Python Cookbook

Author : Chris Albon

Publisher : "O'Reilly Media, Inc."

Page : 285 pages

File Size : 29,65 MB

Release : 2018-03-09

Category : Computers

ISBN : 1491989335

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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



Machine Learning for Hackers

Author : Drew Conway

Publisher : "O'Reilly Media, Inc."

Page : 323 pages

File Size : 47,98 MB

Release : 2012-02-13

Category : Computers

ISBN : 1449330533

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Book Description

If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data



The Hundred-page Machine Learning Book

Author : Andriy Burkov

Publisher :

Page : 141 pages

File Size : 20,94 MB

Release : 2019

Category : Machine learning

ISBN : 9781999579500

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Book Description

Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning.The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue.



Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Author : John D. Kelleher

Publisher : MIT Press

Page : 853 pages

File Size : 47,81 MB

Release : 2020-10-20

Category : Computers

ISBN : 0262361108

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Book Description

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.