Название: Python Machine learning: The comprehensive guide to learn and improve Python programming for Machine learning
Автор: Bill Ruiz
Издательство: Independently published
Год: 2021
Страниц: 112
Язык: английский
Формат: pdf, epub
Размер: 10.19 MB

Artificial Intelligence (AI) is a branch of computer science that seeks to develop computer systems that are capable of human-like intelligence. You can have Artificial Intelligence that replicates the human mind implemented this way rather than just having a computer system that mimics and the entire human brain. The latter is probably something very far off, if it is ever achieved.

Artificial Intelligence systems run independently. Computer systems based on Artificial Intelligence need to be trained, but once trained, they can operate on their own without human intervention. In the case of human intelligence, the more data you are exposed to, the better you get at solving problems related to that data. Similarly, computer systems based on Artificial Intelligence self-adjust to make themselves perform better.

This is quite a contrast with conventional computer systems, which only do what you tell them to do, and without humans rewriting the programs that run them, they don’t get any better at what they do. This is a crucial point to focus on, because the kinds of systems that we are going to talk about in this book will adjust themselves and get better, without any human intervention whatsoever. Once they are deployed, the human operators might not even understand why the artificially intelligent computer system makes the decisions it does, or how it is making those decisions.

Contents:

Introduction
chaᴘtеr 1: Python installation
chaᴘtеr 2: Python for machinе lеarning
chaᴘtеr 3: data scrubbing
chaᴘtеr 4: data mining catеgoriеs
chaᴘtеr 5: diffеrеncе bеtwееn Machinе Lеarning and AI
chaᴘtеr 7: linеar rеgrеssion with Python
chaᴘtеr 8: fеaturе еnginееring
chaᴘtеr 9: how do conᴠolutional nеural nеtworᴋs worᴋ?
chaᴘtеr 10: toᴘ AI framеworᴋs and Machinе Lеarning librariеs Tеnѕоrflоw
chaᴘtеr 11: thе futurе of Machinе Lеarning
conclusion








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