Artificial Intelligence and Machine Learning Fundamentals

Artificial Intelligence and Machine Learning Fundamentals
Author :
Publisher : Packt Publishing Ltd
Total Pages : 330
Release :
ISBN-10 : 9781789809206
ISBN-13 : 1789809207
Rating : 4/5 (207 Downloads)

Book Synopsis Artificial Intelligence and Machine Learning Fundamentals by : Zsolt Nagy

Download or read book Artificial Intelligence and Machine Learning Fundamentals written by Zsolt Nagy and published by Packt Publishing Ltd. This book was released on 2018-12-12 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create AI applications in Python and lay the foundations for your career in data science Key FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail with interesting examplesMaster core AI concepts with engaging activitiesBook Description Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills! What you will learnUnderstand the importance, principles, and fields of AIImplement basic artificial intelligence concepts with PythonApply regression and classification concepts to real-world problemsPerform predictive analysis using decision trees and random forestsCarry out clustering using the k-means and mean shift algorithmsUnderstand the fundamentals of deep learning via practical examplesWho this book is for Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).


Artificial Intelligence and Machine Learning Fundamentals Related Books

Artificial Intelligence and Machine Learning Fundamentals
Language: en
Pages: 330
Authors: Zsolt Nagy
Categories: Computers
Type: BOOK - Published: 2018-12-12 - Publisher: Packt Publishing Ltd

DOWNLOAD EBOOK

Create AI applications in Python and lay the foundations for your career in data science Key FeaturesPractical examples that explain key machine learning algori
Machine Learning Fundamentals
Language: en
Pages: 240
Authors: Hyatt Saleh
Categories: Computers
Type: BOOK - Published: 2018-11-29 - Publisher: Packt Publishing Ltd

DOWNLOAD EBOOK

With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and take application perfo
Artificial Intelligence
Language: en
Pages: 48
Authors: Tim D. Washington
Categories: Computers
Type: BOOK - Published: 2019-02-27 - Publisher: Independently Published

DOWNLOAD EBOOK

What is Artificial Intelligence? Artificial intelligence is a system that tends to simulate intelligent behaviors into computer-controlled machines or digital c
Fundamentals of Deep Learning
Language: en
Pages: 272
Authors: Nikhil Buduma
Categories: Computers
Type: BOOK - Published: 2017-05-25 - Publisher: "O'Reilly Media, Inc."

DOWNLOAD EBOOK

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern m
Fundamentals of Artificial Intelligence
Language: en
Pages: 730
Authors: K.R. Chowdhary
Categories: Computers
Type: BOOK - Published: 2020-04-04 - Publisher: Springer Nature

DOWNLOAD EBOOK

Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satis