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The Hitchhiker's Guide to Machine Learning Algorithms

The Hitchhiker's Guide to Machine Learning Algorithms
Author: Devin Schumacher
Publisher: SERP Media
Total Pages: 351
Release: 2023-07-26
Genre: Computers
ISBN:
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Hello humans & welcome to the world of machines! Specifically, machine learning & algorithms. We are about to embark on an exciting adventure through the vast and varied landscape of algorithms that power the cutting-edge field of artificial intelligence. Machine learning is changing the world as we know it. From predicting stock market trends and diagnosing diseases to powering the virtual assistants in our smartphones and enabling self-driving cars, and picking up the slack on your online dating conversations. What makes this book unique is its structure and depth. With 100 chapters, each dedicated to a different machine learning concept, this book is designed to be your ultimate guide to the world of machine learning algorithms. Whether you are a student, a data science professional, or someone curious about machine learning, this book aims to provide a comprehensive overview that is both accessible and in-depth. The algorithms covered in this book span various categories including: Classification & Regression: Learn about algorithms like Decision Trees, Random Forests, Support Vector Machines, and Logistic Regression which are used to classify data or predict numerical values. Clustering: Discover algorithms like k-Means, Hierarchical Clustering, and DBSCAN that group data points together based on similarities. Neural Networks & Deep Learning: Dive into algorithms and architectures like Perceptrons, Convolutional Neural Networks (CNN), and Long Short-Term Memory Networks (LSTM). Optimization: Understand algorithms like Gradient Descent, Genetic Algorithms, and Particle Swarm Optimization which find the best possible solutions in different scenarios. Ensemble Methods: Explore algorithms like AdaBoost, Gradient Boosting, and Random Forests which combine the predictions of multiple models for improved accuracy. Dimensionality Reduction: Learn about algorithms like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) which reduce the number of features in a dataset while retaining important information. Reinforcement Learning: Get to know algorithms like Q-learning, Deep Q-Network (DQN), and Monte Carlo Tree Search which are used in systems that learn from their environment. Each chapter is designed as a standalone introduction to its respective algorithm. This means you can start from any chapter that catches your interest or proceed sequentially. Along with the theory, practical examples, applications, and insights into how these algorithms work under the hood are provided. This book is not just an academic endeavor but a bridge that connects theory with practical real-world applications. It's an invitation to explore, learn, and harness the power of algorithms to solve complex problems and make informed decisions. Fasten your seat belts as we dive into the mesmerizing world of machine learning algorithms. Whether you are looking to expand your knowledge, seeking inspiration, or in pursuit of technical mastery, this book should sit on your coffee table and make you look intelligent in front of all invited (and uninvited) guests.

Machine Learning Algorithms

Machine Learning Algorithms
Author: Giuseppe Bonaccorso
Publisher:
Total Pages:
Release: 2017
Genre: Computer algorithms
ISBN: 9781523112210
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Mastering Machine Learning Algorithms

Mastering Machine Learning Algorithms
Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
Total Pages: 799
Release: 2020-01-31
Genre: Computers
ISBN: 1838821910
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Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key FeaturesUpdated to include new algorithms and techniquesCode updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applicationsBook Description Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios. What you will learnUnderstand the characteristics of a machine learning algorithmImplement algorithms from supervised, semi-supervised, unsupervised, and RL domainsLearn how regression works in time-series analysis and risk predictionCreate, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANsWho this book is for This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.

Mastering Machine Learning Algorithms

Mastering Machine Learning Algorithms
Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
Total Pages: 567
Release: 2018-05-25
Genre: Computers
ISBN: 1788625900
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Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.

Machine Learning for Beginners

Machine Learning for Beginners
Author: William J Ford
Publisher:
Total Pages: 200
Release: 2020-02-18
Genre:
ISBN:
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You Are About To Start Your Journey To Understanding Machine Learning Like The Back Of Your Hand And Use It To Your Advantage! If you've always wanted to learn how computers are able to perform some complex things like suggesting which products to buy to different customers depending on their buying behavior and much more, but you've never pictured yourself enrolling in a computer science class to study everything from scratch, then keep reading... Are you already tired about your business' poor performance or your limited ability to take advantage of modern technology to make your life easier because you don't understand the technology that every other successful person around you is leveraging every day? Have you tried using pre-built computer software to optimize your business but you never get the output you desire? Do you want to stop spending too much money on tech experts to assist you with tasks that you can do yourself with a little effort to learn, and increase efficiency? If so, then you've come to the right place. You see, having a successful online business or improving your knowledge about computer systems as regards to making accurate predictions for whatever goal doesn't have to be difficult- even if you are not ready to enroll for a computer science or IT course. In fact, it's easier than you think. One article published in Elite Data Science proves that machine learning can help us lead happier and healthier lives, especially with the recent breakthroughs in deep learning (that involves imitation of brain neurons). Another one published in Analytics Training asserts that learning machine learning is one of the best ways to guarantee success in many areas of the modern world, including business. Another article in Towards Data Science asserts that employing machine learning in manufacturing is the best way to improve efficiency, save money and time. That means that understanding machine learning, that tiny part of Artificial intelligence, can impact your daily life and businesses greatly. The question is.... Where do you even start? How do you build your understanding of machine learning to a point where you can start using it to make your life better? What are the critical things you need to learn about machine learning to get started as a beginner? What areas of your life can you apply your knowledge of machine learning? If you have these and other related questions, this book is for you so keep reading. Here's just a tiny fraction of what you'll discover in this book: The history of machine learning What machine learning can do for your business The machine learning algorithms How to develop a machine learning model from start to finish How neural networks work in machine learning The auto-encoders What you need to know about EM algorithm and how it is applied Logistic regression for machine learning The theory and setup in deep learning with tensorflow Natural language processing Data cleansing ...and much, much more! Take a second to imagine how you'd feel understanding how to can control a computer system to deliver output as you desire, without hiring an "expert", or at least understanding how computers make predictions. If you really want to find out how life would be once you know how to harness the power of modern technology to do anything you want, even if you are a complete beginner, Scroll up and click Buy Now With 1-Click or Buy Now to get started!

Machine Learning For Dummies

Machine Learning For Dummies
Author: John Paul Mueller
Publisher: John Wiley & Sons
Total Pages: 471
Release: 2021-02-09
Genre: Computers
ISBN: 1119724015
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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 Made Easy: A Beginner's Guide for All

Machine Learning Made Easy: A Beginner's Guide for All
Author: M.B. Chatfield
Publisher: M.B. Chatfield
Total Pages: 258
Release:
Genre: Computers
ISBN:
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Unleash the power of machine learning to automate tasks, make predictions, and solve complex problems. Machine learning is a powerful tool that can be used to automate tasks, make predictions, and solve complex problems. It is used in a wide variety of industries, including healthcare, finance, and manufacturing. Machine Learning Made Easy is the perfect resource for anyone who wants to learn the basics of machine learning. This comprehensive guide covers everything you need to know, from the basics of machine learning algorithms to advanced topics such as deep learning. Whether you're a student, a business professional, or a data enthusiast, Machine Learning Made Easy is the essential resource for learning about machine learning. Here are some of the key topics covered in the book: Introduction to machine learning Types of machine learning algorithms Choosing the right machine learning algorithm Training a machine learning model Evaluating a machine learning model Using machine learning to automate tasks Using machine learning to make predictions If you are a beginner who wants to learn about machine learning, Machine Learning Made Easy is a great place to start.

Machine Learning for Beginners

Machine Learning for Beginners
Author: Steven Cooper
Publisher: Roland Bind
Total Pages: 85
Release: 2018-09-07
Genre: Computers
ISBN:
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If you are looking for a complete beginners guide to learn machine learning with examples, in just a few hours, then you need to continue reading. 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. ★★ Grab your copy today and learn ★★ ♦ The different types of learning algorithm that you can expect to encounter ♦ The numerous applications of machine learning ♦ The different types of machine learning and how they differ ♦ The best practices for picking up machine learning ♦ What languages and libraries to work with ♦ The future of machine learning ♦ The various problems that you can solve with machine learning algorithms ♦ And much more... Starting from nothing, we slowly work our way through all the concepts that are central to machine learning. By the end of this book, you're going to feel as though you have an extremely firm understanding of what machine learning is, how it can be used, and most importantly, how it can change the world. You're also going to have an understanding of the logic behind the algorithms and what they aim to accomplish. Don't waste your time working with a book that's only going to make an already complicated topic even more complicated. Scroll up and click the buy now button to learn everything you need to know about Machine Learning!

Machine Learning Algorithms

Machine Learning Algorithms
Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
Total Pages: 360
Release: 2017-07-24
Genre: Computers
ISBN: 1785884514
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Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.

Machine Learning

Machine Learning
Author: Ryan Roberts
Publisher: Createspace Independent Publishing Platform
Total Pages: 53
Release: 2017-07-29
Genre: Computer algorithms
ISBN: 9781974020386
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Machine Learning Sale price. You will save 66% with this offer. Please hurry up! The Ultimate Beginners Guide For Neural Networks, Algorithms, Random Forests and Decision Trees Made Simple From smart bulbs to self-driving cars, intelligent machines are becoming ever more prevalent in our day to day lives. The underpinning of this technology is called machine learning, and is the same basic concept that is used by marketing experts to target ads on webpages and collect data about their customers. The uses for machine learning in today's world are vast and ever expanding. The technology is poised to revolutionize the way people interact with machines on a daily basis. Understanding just how these programs and processes function can help you to navigate this new technology. Here is a preview of what you'll learn: Just what machine learning is and why it's important Supervised versus unsupervised algorithms and the potential uses of each Description of some of the most popular machine learning algorithms The role of machine learning in programs like Cortana, Alexa, Siri, or Google assist If you're not familiar with the possibilities of machine learning, you'll be surprised to see the variety of ways it can be utilized beyond the much-publicized aspects like speech recognition. This book can be your first step into that larger world. Download your copy of " Machine Learning " by scrolling up and clicking "Buy Now With 1-Click" button. Tags: Machine Learning, Machine Learning Algorithms, Machine Learning Course, Big Data Machine Learning, Machine Learning For Dummies, Machine Learning Big Data, Machine Learning Tools, Machine Learning Basics, Machine Learning Online Course, Learn Machine Learning, Machine Learning As A Service, Cloud Machine Learning, Big Data And Machine Learning, Machine Learning And Big Data, Machine Learning Algorithms For Beginners, Machine Learning Platform, Data Science, Machine Learning Big Data Analytics, Machine Learning Companies, Ai Machine Learning, Machine Learning Cloud, Machine Learning Services