For More Content Visit NulledPremium >>> NulledPremium.com



Book details
Format: pdf
File Size: 16 MB
Print Length: 162 pages
Publisher: Packt Publishing; 1 edition (31 July 2018)
Sold by: Amazon Asia-Pacific Holdings Private Limited
Language: English
ASIN: B07G47PJKB

Who this book is for

Python Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with code.

Key Features

Explore a variety of AI projects with Python
Get well-versed with different types of neural networks and popular deep learning algorithms
Leverage popular Python deep learning libraries for your AI projects
Book Description
Artificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence.
This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library.

By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progress

What you will learn
Build a prediction model using decision trees and random forest
Use neural networks, decision trees, and random forests for classification
Detect YouTube comment spam with a bag-of-words and random forests
Identify handwritten mathematical symbols with convolutional neural networks
Revise the bird species identifier to use images
Learn to detect positive and negative sentiment in user reviews

Table of Contents

Building Your Own Prediction Models
Prediction with Random Forests
Application for comment classification
Neural Networks
Deep Learning