Название: Machine Learning: 2 Books in 1: An Introduction Math Guide for Beginners to Understand Data Science Through the Business ApplicationsАвтор: Samuel Hack
Издательство: Amazon.com Services LLC
Год: 2020
Страниц: 168
Язык: английский
Формат: pdf, azw3
Размер: 11.6 MB
It’s necessary to have a solid understanding of statistics and quantitative analysis to be a data scientist. After all, Artificial Intelligence and Machine Learning are rooted in statistics. This provides the anchor and foundation for the kind of mathematics needed.
While coding is not required to understand this book, it is a major component of Machine Learning. In order to handle large volumes of data, data scientists need to have a working knowledge of computer programming to "tell" the data what they want it to do. This book will not offer much in the way of coding information, but it will present resources and avenues to get you started in studying coding on your own. By the end of the book, I will at least assist you in setting up Python with the necessary libraries and toolkits to help you start learning to code.
The most common language used in Machine Learning is Python. It’s a versatile language that is relatively easy to learn and freely available. There are Python packages designed for data analysis to make your coding go faster. C++ is also quite common but more difficult to master. A third option is R, which is quite popular because it is free and open source. Students often use it because of its availability and simplicity. The drawback of using R is that it can't handle massive datasets often used in machine learning and artificial intelligence, which is somewhat limiting.
With a wide range of comprehensive advice including machine learning models, neural networks, statistics, and much more, this guide is a highly effective tool for mastering this incredible technology.
In book one, you'll learn:
What is Artificial Intelligence Really, and Why is it So Powerful?
Choosing the Right Kind of Machine Learning Model for You
An Introduction to Statistics
Reinforcement Learning and Ensemble Modeling
“Random Forests” and Decision Trees
In book two, you'll learn:
Learn the Fundamental Concepts of Machine Learning Algorithms
Understand The Four Fundamental Types of Machine Learning Algorithm
Master the Concept of “Statistical Learning
Learn Everything You Need to Know about Neural Networks and Data Pipelines
Master the Concept of “General Setting of Learning”
A Free Bonus
And Much More!
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