國立虎尾科技大學 |

Learn AI with Python = explore machine learning and deep learning techniques for building smart AI systems using scikit-learn, NLTK, NeuroLab, and Keras /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Learn AI with Python / Gaurav Leekha.
Reminder of title:	explore machine learning and deep learning techniques for building smart AI systems using scikit-learn, NLTK, NeuroLab, and Keras /
Author:	Leekha, Gaurav.
Description:	1 online resource (272 pages)
Subject:	COMPUTERS / Data Science / Machine Learning. -
Online resource:	https://portal.igpublish.com/iglibrary/search/BPB0000547.html
ISBN:	9789391392611

Learn AI with Python = explore machine learning and deep learning techniques for building smart AI systems using scikit-learn, NLTK, NeuroLab, and Keras /
Leekha, Gaurav.

Learn AI with Python explore machine learning and deep learning techniques for building smart AI systems using scikit-learn, NLTK, NeuroLab, and Keras /[electronic resource] ：Gaurav Leekha. - 1 online resource (272 pages)

Includes bibliographical references and index.

Learn AI with Python : explore machine learning and deep learning techniques for building smart AI systems using scikit-learn, NLTK, NeuroLab, and Keras -- About the Author -- About the Reviewer -- Acknowledgement -- Preface -- Table of Contents -- Chapter 1. Introduction to AI and Python -- Chapter 2. Machine Learning and Its Algorithms -- Chapter 3. Classification and Regression Using Supervised Learning -- Chapter 4. Clustering Using Unsupervised Learning -- Chapter 5. Solving Problems with Logic Programming -- Chapter 6. Natural Language Processing with Python -- Chapter 7. Implementing Speech Recognition with Python -- Chapter 8. Implementing Artificial Neural Network (ANN) with Python -- Chapter 9. Implementing Reinforcement Learning with Python -- Chapter 10. Implementing Deep Learning and Convolutional Neural Network -- Index.

Access restricted to authorized users and institutions.

The book ‘Learn AI with Python’ is intended to provide you with a thorough understanding of artificial intelligence as well as the tools necessary to create your intelligent applications. This book introduces you to artificial intelligence and walks you through the process of establishing an AI environment on a variety of platforms. It dives into machine learning models and various predictive modeling techniques, including classification, regression, and clustering. Additionally, it provides hands-on experience with logic programming, ASR, neural networks, and natural language processing through real-world examples and fully functional Python implementation. Finally, the book deals with profound models of learning such as R-CNN and YOLO. Object detection in images is also explained in detail using Convolutional Neural Networks (CNNs), which are also explained. By the end of this book, you will have a firm grasp of machine learning and deep learning techniques, as well as a steered methodology for formulating and solving related problems.

Mode of access: World Wide Web.

ISBN: 9789391392611Subjects--Topical Terms:

1483854
COMPUTERS / Data Science / Machine Learning.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: Q325.5

Dewey Class. No.: 006.3

Learn AI with Python = explore machine learning and deep learning techniques for building smart AI systems using scikit-learn, NLTK, NeuroLab, and Keras /
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506 # $a Access restricted to authorized users and institutions.
520 3 $a The book ‘Learn AI with Python’ is intended to provide you with a thorough understanding of artificial intelligence as well as the tools necessary to create your intelligent applications. This book introduces you to artificial intelligence and walks you through the process of establishing an AI environment on a variety of platforms. It dives into machine learning models and various predictive modeling techniques, including classification, regression, and clustering. Additionally, it provides hands-on experience with logic programming, ASR, neural networks, and natural language processing through real-world examples and fully functional Python implementation. Finally, the book deals with profound models of learning such as R-CNN and YOLO. Object detection in images is also explained in detail using Convolutional Neural Networks (CNNs), which are also explained. By the end of this book, you will have a firm grasp of machine learning and deep learning techniques, as well as a steered methodology for formulating and solving related problems.
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