Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning Paperback - July 24, 2017
by Giuseppe Bonaccorso
Build a strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide
- 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.
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.
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.
- 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
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.
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World Kindle Edition
by Pedro Domingos
A spell-binding quest for the one algorithm capable of deriving all knowledge from data, including a cure for cancer
Society is changing, one learning algorithm at a time, from search engines to online dating, personalized medicine to predicting the stock market. But learning algorithms are not just about Big Data - these algorithms take raw data and make it useful by creating more algorithms. This is something new under the sun: a technology that builds itself. In The Master Algorithm, Pedro Domingos reveals how machine learning is remaking business, politics, science, and war. And he takes us on an awe-inspiring quest to find 'The Master Algorithm' - a universal learner capable of deriving all knowledge from data.
Machine Learning: Algorithms and Applications 1st Edition, Kindle Edition
by Mohssen Mohammed
Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.
Machine Learning Algorithms From Scratch with Python
You must understand algorithms to get good at machine learning. The problem is that they are only ever explained using Math. No longer. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work. Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch.