Machine Learning for Absolute Beginners

Author: Oliver Theobald
Publisher:
ISBN: 9781520951409
Format: PDF, ePub, Mobi
Download Now
Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile? Well, hold on there... Before you embark on your epic journey into the world of machine learning, there is basic theory to march through first. But rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this book offers a practical and high-level introduction to machine learning. Machine Learning for Absolute Beginners has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home. This title opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into introducing specific algorithms applied in machine learning, including their pros and cons. At the end of the book, I share insights and advice on further learning and careers in this space. Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle deep learning and Scikit-learn, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and offer you a clear lay of the land. In this step-by-step guide you will learn: - The very basics of Machine Learning that all beginners need to master - Association Analysis used in the retail and E-commerce space - Recommender Systems as you've seen online, including Amazon - Decision Trees for visually mapping and classifying decision processes - Regression Analysis to create trend lines and predict trends - Data Reduction and Principle Component Analysis to cut through the noise - k-means and k-nearest Neighbor (k-nn) Clustering to discover new data groupings - Introduction to Deep Learning/Neural Networks - Bias/Variance to optimize your machine learning model - How to build your first machine learning model to predict video game sales using Python - Careers in the field Please also note that under Amazon's Matchbook program, the purchaser of this book can add the Kindle version of this title (valued at $3.99 USD) to their Amazon Kindle library at no cost.

Machine Learning for Absolute Beginners

Author: Oliver Theobald
Publisher: Independently Published
ISBN: 9781549617218
Format: PDF, Kindle
Download Now
"The manner in which computers are now able to mimic human thinking to process information is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the modern age of machine learning, computers do not strictly need to receive an 'input command' to perform a task, but rather 'input data'. From the input of data they are able to form their own decisions and take actions virtually as a human world. But given it is a machine, it can consider many more scenarios and execute far more complicated calculations to solve complex problems. This is the element that excites data scientists and machine learning engineers the most. The ability to solve complex problems never before attempted. This book will dive in to introduce machine learning, and is ideal for beginners starting out in machine learning."--page 4 of cover.

Machine Learning For Dummies

Author: John Paul Mueller
Publisher: John Wiley & Sons
ISBN: 111924577X
Format: PDF
Download Now
Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

Machine Learning for Absolute Beginners

Author: Jennifer Grange
Publisher: Createspace Independent Publishing Platform
ISBN: 9781979613095
Format: PDF, ePub, Docs
Download Now
Do you understand the difference between supervised and unsupervised learning algorithms? Would you like to be able to learn about this quickly, in a simple and concise way? Some of the world's biggest companies use machine learning techniques to manage their functions in an effective and professional way that is beneficial to their business. Now you can understand more about them with Machine Learning for Absolute Beginners, a book which provides you with in-depth knowledge of the Supervised and Unsupervised Learning Algorithms which play such a big part and specifically: Supervised learning Semi-supervised learning Learning to learn Transduction Learning that reinforces Written in a clear and easy-to-follow style, this book takes what might seem to be a complex subject and simplifies it in basic layman's terms, so that it becomes an easy read, even if you are a complete novice. Get yourself a copy of Machine Learning for Absolute Beginners today. It will enhance your understanding and provide you with everything you'll need to expand your knowledge of this fascinating subject!

Machine Learning For Beginners Guide Algorithms

Author: William Sullivan
Publisher: PublishDrive
ISBN: 197563232X
Format: PDF, ePub
Download Now
Machines can LEARN ?!?! Machine learning occurs primarily through the use of " algorithms" and other elaborate procedures Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning Note: Bonus chapters included inside! Instead of spending hundreds or even thousands of dollars on courses/materials why not read this book instead? Its a worthwhile read and the most valuable investment you can make for yourself Other books easily retail for $50-$100+ and have far less quality content. This book is by far superior and exceeds any other book available for beginners. What You'll Learn Supervised Learning Unsupervised Learning Reinforced Learning Algorithms Decision Tree Random Forest Neural Networks Python Deep Learning And much, much more! This is the most comprehensive and easy to read step by step guide in machine learning that exists. Learn from one of the most reliable programmers alive and expert in the field You do not want to miss out on this incredible offer!

Machine Learning

Author: Ethem Alpaydin
Publisher: MIT Press
ISBN: 0262529513
Format: PDF, Kindle
Download Now
A concise overview of machine learning -- computer programs that learn from data -- which underlies applications that include recommendation systems, face recognition, and driverless cars.

Machine Learning in Python

Author: Michael Bowles
Publisher: John Wiley & Sons
ISBN: 1118961749
Format: PDF, ePub
Download Now
This book shows readers how they can successfully analyze data using only two core machine learning algorithms---and how to do so using the popular Python programming language. These algorithms deal with common scenarios faced by all data analysts and data scientists. This book focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers a multitude of use cases (what ad to place on a web page, predicting prices in securities markets, detecting credit card fraud, etc.). The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code. The author will explain in simple terms, using no complex math, how these algorithms work, and will then show how to apply them in Python. He will also provide advice on how to select from among these algorithms, and will show how to prepare the data, and how to use the trained models in practice. The author begins with an overview of the two core algorithms, explaining the types of problems solved by each one. He then introduces a core set of Python programming techniques that can be used to apply these algorithms. The author shows various techniques for building predictive models that solve a range of problems, from simple to complex; he also shows how to measure the performance of each model to ensure you use the right one. The following chapters provide a deep dive into each of the two algorithms: penalized linear regression and ensemble methods. Chapters will show how to apply each algorithm in Python. Readers can directly use the sample code to build their own solutions.

Understanding Machine Learning

Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Format: PDF, Docs
Download Now
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Data Analytics for Absolute Beginners

Author: O. Theobald
Publisher:
ISBN: 9781521159453
Format: PDF, Docs
Download Now
Start here if you are: A marketing professional, financial analyst, politician, CEO, professional coach, student or a decision maker in an organization. This book is the start of the road to becoming a data scientist or data literate professional. In today's modern world it's vital t to understand data analytics. This includes the various processes, resources, advantages and limitations of data analytics. It's important that you can grasp the terminology and basic concepts of data analytics just as much as you need to understand basic accounting and financial literacy to be a successful decision maker in the business world. This book is ideal for anyone who is interested in making sense of data analytics without the assumption that you understand specific data science terminology or advanced programming languages. Topics covered: Regression AnalysisData MiningMachine Learning Data ReductionClusteringAnomaly DetectionText MiningAssociation AnalysisData Visualization

Bayesian Reasoning and Machine Learning

Author: David Barber
Publisher: Cambridge University Press
ISBN: 0521518148
Format: PDF, Docs
Download Now
A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.