Free Machine Learning For Dummies Ebooks Online
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!
Series: For Dummies
Paperback: 432 pages
Publisher: For Dummies; 1 edition (May 31, 2016)
Language: English
ISBN-10: 1119245516
ISBN-13: 978-1119245513
Product Dimensions: 7.4 x 0.9 x 9.3 inches
Shipping Weight: 1.2 pounds (View shipping rates and policies)
Average Customer Review: 3.8 out of 5 stars See all reviews (20 customer reviews)
Best Sellers Rank: #60,972 in Books (See Top 100 in Books) #50 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Intelligence & Semantics #767 in Books > Textbooks > Computer Science
This introductory text on Machine Learning provides all the basics a budding Data Scientist or Onlooker from any Application Domain that could use Machine Learning Expertise needs to get started. I've read more than my share of White Papers from Software Companies and would be Consultants seeking to sell a prepackaged solution for Machine Learning and also struggled to understand academic papers with more mathematics and specialized vocabulary than I was able to follow; this book provides both the context for understanding what Machine Learning can do and the basic tools necessary to try Real World Algorithms and Datasets. Yes, it does provide instruction for installing R-Studio and Anaconda Python in all the most common OS environments; but it also provides basic math background in Linear Algebra as well as Statistics, primers in basic programming in R and Python and a selection of the most common Machine Learning Algorithms and code for implemented examples of K-Nearest Neighbors, Classifiers, Neural Nets and Logistic Regression.No you won't learn everything you need to know to get a job as a Machine Learning expert or Data Scientist; but after reading this book and running several of the examples you would be ready to implement small examples with your own Datasets and then learn such application frameworks asTheano, Scikit-Learn, or TensorFlow. The authors also have written an excellent guidebook: "Python for Data Science for Dummies".
Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python) Unsupervised Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python and Theano (Machine Learning in Python) Deep Learning in Python Prerequisites: Master Data Science and Machine Learning with Linear Regression and Logistic Regression in Python (Machine Learning in Python) Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow (Machine Learning in Python) Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow (Machine Learning in Python) Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) Unsupervised Machine Learning in Python: Master Data Science and Machine Learning with Cluster Analysis, Gaussian Mixture Models, and Principal Components Analysis Machine Learning with Spark - Tackle Big Data with Powerful Spark Machine Learning Algorithms Foundations of Machine Learning (Adaptive Computation and Machine Learning series) Introduction to Machine Learning (Adaptive Computation and Machine Learning series) Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) First-Time Machine Applique: Learning to Machine Applique in Nine Easy Lessons A collection of Advanced Data Science and Machine Learning Interview Questions Solved in Python and Spark (II): Hands-on Big Data and Machine ... Programming Interview Questions) (Volume 7) Machine Learning For Dummies Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning series) Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series) Learning Deep Architectures for AI (Foundations and Trends(r) in Machine Learning) The Bread Lover's Bread Machine Cookbook: A Master Baker's 300 Favorite Recipes for Perfect-Every-Time Bread-From Every Kind of Machine More Bread Machine Magic : More Than 140 New Recipes From the Authors of Bread Machine Magic for Use in All Types of Sizes of Bread Machines
