Pdf Book Name: Python Artificial Intelligence Projects for Beginners: Get up and running with Artificial Intelligence using 8 smart and exciting AI applications
Author: Joshua Eckroth
Publisher: Packt Publishing
Pages: 162 / 155
File size: 16.12 MB
File format: PDF
Python Artificial Intelligence Projects for Beginners: Get up and running with Artificial Intelligence using 8 smart and exciting AI applications Pdf Book Description:
Artificial Intelligence (AI) is the most recent technology that is being used among diverse companies, industries, and industries. Python Artificial Intelligence Projects for Beginners shows AI jobs in Python, covering contemporary methods which compose the world of Artificial Intelligence. This publication starts with helping you to construct your very first forecast model utilizing the favorite Python library, scikit learn. This book is for Python developers who want to take their first step in the world of artificial intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you can play around with the code., Building Your Own Prediction Models, introduces classification and techniques for evaluation, and then explains decision trees, followed by a coding project in which a predictor for student performance is built. Deep Learning, discusses deep learning and CNNs. You will practice convolutional neural networks and deep learning with two projects. First, you will build a system that can read handwritten mathematical symbols and then revisit the bird species identifier and change the implementation to use a deep convolutional neural network that is significantly more accurate.
You may know how to create a classifier with a successful machine learning technique, arbitrary woods, and decision trees. With intriguing projects on calling bird species, assessing student performance information, tune genre identification, and spam detection, you will learn the principles and various techniques and algorithms which boost the growth of those wise applications. In the concluding chapters, you’ll also understand profound learning and neural network mechanics by means of these jobs with the support of this Keras library. Building Your Own Prediction Models, introduces classification and techniques for evaluation, and then explains decision trees, followed by a coding project in which a predictor for student performance is built. Applications for Comment Classification, introduces text processing and the bag of words technique. Then shows how this technique can be used to build a spam detector for YouTube comments. Next, you will learn about the sophisticated Word2Vec model and practice it with a coding project that detects positive and negative product, restaurant, and movie reviews.
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