Make Machine Learning Algorithms on your Tips

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.
Book Description

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.

Information Theory, Inference, and Learning Algorithms Hardcover - 25 Sep 2003

by David J. C. MacKay

Information theory and inference taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks.

Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models Paperback - May 25, 2018

by Giuseppe Bonaccorso

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 sci-kit-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.

Machine learning Beginners Guide Algorithms: Supervised & Unsupervised learning, Decision Tree & Random Forest Introduction (1) Paperback -August 20, 2017

by William Sullivan

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

Instead of spending hundreds or even thousands of dollars on courses/materials why not read this book instead? It's a worthwhile read and the most valuable investment you can make for yourself

Machine Learning for Beginners: A Plain English Introduction to Artificial Intelligence and Machine Learning Kindle Edition

by John Slavio

This book is an introduction to basic machine learning and artificial intelligence. It gives you a list of applications, and also a few examples of the different types of machine learning.

Here's What You'll Learn in this Book:

Introduction to Machine Learning
Different Applications of Machine Learning
Introduction to Statistics for Machine Learning
Supervised Learning
Unsupervised Learning
Reinforced Learning
Conclusion

Source: HOB

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