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 Название: DATA SCIENCE FROM SCRATCH: Complete guide to master data process, mining, data-analytic, neural networks, machine learning in Python, Linear Algebra, Statistics, Coding, Applications Decision Tree
Автор: G.S. Collins
Издательство: Amazon.com Services LLC
Год: 2020
Формат: epub/pdf(conv.)
Страниц: 151
Размер: 10.4 Mb
Язык: English
Become the master of machine learning with this powerful guide.
Do you want to know more about neural networks? Have you heard of machine learning, but you’re not sure where to begin?
Written with the beginner in mind, this detailed guide breaks down everything you need to know about deep and machine learning in a simple, easy-to-understand way. Machine learning is a fascinating and ever-growing field, and its development will shape our futures. Now, you can understand what makes this topic so powerful no matter your level of experience.
Using the popular and much-loved programming language Python, inside this comprehensive guide, you will:
Learn How to Get Started with Jupyter Notebooks
Understand Python Using Various Data Structures
Perform Object Oriented Programming Using Python
Use The Most Common Libraries Including Numpy, Matplotlib, and Pandas
Learn and Recap on The Basics of Linear Algebra and Statistics
Comprehend Machine Learning Algorithms Like Linear Regression, Logistic Regression, K-nearest neighbors and Decision Trees
Discover Deep Learning Concepts Like Convolutional Neural Networks and Recurrent Neural Networks
Implement CNNs and RNNs using Keras Deep Learning Framework
And More!
With a wide variety of vital topics, this book is your all-in-one ticket to understanding machine learning. Plus, you’ll also learn bonus content, such as Generative Adversarial Network (GAN) models and why they’re so important. With simple explanations designed to get you comfortable with the maths and statistics behind machine learning, this book is perfect for both the novice and the pro!
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Автор: bomboane 11-02-2020, 12:33 | Напечатать |
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Data Science from Scratch: Complete guide to master data process 
