Artificial Intelligence and Machine Learning Fundamentals: Course, #3

By Vineeta Prasad

This is a comprehensive course outline for Artificial Intelligence and Machine Learning that covers various important topics in the field. The course starts with an introduction to AI and ML, including their definitions, history, applications, and ethical and social implications. The second part of the course focuses on Supervised Learning and includes regression analysis, specifically simple linear regression and multiple linear regression, as well as various classification algorithms such as K-Nearest Neighbor (KNN), Decision Trees, Support Vector Machines (SVM), and Naive Bayes. Evaluation metrics for Supervised Learning such as accuracy, precision, recall, and F1 Score are also covered.

The third part of the course covers Unsupervised Learning and includes clustering algorithms such as K-Means Clustering and Hierarchical Clustering, as well as dimensionality reduction techniques such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD). The fourth part of the course focuses on Deep Learning and covers artificial neural networks, including feedforward neural networks, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), as well as optimization techniques such as Stochastic Gradient Descent (SGD) and Backpropagation.

The fifth part of the course covers Reinforcement Learning, including Markov Decision Processes (MDP), Q-Learning, Monte Carlo Methods, and Temporal-Difference Learning. The sixth part of the course focuses on Natural Language Processing and covers various text preprocessing techniques such as tokenization, stopword removal, stemming and lemmatization, as well as N-Grams, sentiment analysis, and Named Entity Recognition (NER).

The conclusion of the course provides a recap of key concepts, the future of AI and ML, career opportunities in AI and ML, and final thoughts and recommendations for further study. This course outline provides a solid foundation for anyone interested in learning about Artificial Intelligence and Machine Learning.

• Language: English
• Publisher: Vineeta Prasad
• Release date: Mar 9, 2023
• ISBN: 9798215109533

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Artificial Intelligence and Machine Learning Fundamentals

While every precaution has been taken in the preparation of this book, the publisher assumes no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein.

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS

First edition. March 9, 2023.

Copyright © 2023 Vineeta Prasad.

ISBN: 979-8215109533

Written by Vineeta Prasad.

Table of Contents

Title Page

Copyright Page

Artificial Intelligence and Machine Learning Fundamentals (Course, #3)

I. Introduction to Artificial Intelligence and Machine Learning

II. Supervised Learning

III. Unsupervised Learning

IV. Deep Learning

V. Reinforcement Learning

VI. Natural Language Processing

VII. Conclusion

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About the Author

By : Vineeta Prasad

Copyright © 2023 [Vineeta Prasad]. All rights reserved.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of the copyright owner.

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Table of Contents

I. Introduction to Artificial Intelligence and Machine Learning

A. Definition of Artificial Intelligence and Machine Learning

B. History of AI and ML

C. Applications of AI and ML

D. Ethics and Social Implications of AI and ML

II. Supervised Learning

A. Regression Analysis  

1. Simple Linear Regression  2. Multiple Linear Regression

B. Classification Algorithms

1. K-Nearest Neighbor (KNN)  2. Decision Trees

3. Support Vector Machines (SVM)  4. Naive Bayes

C. Evaluation Metrics for Supervised Learning

1. Accuracy  2. Precision and Recall  3. F1 Score

III. Unsupervised Learning

A. Clustering Algorithms

1. K-Means Clustering  2. Hierarchical Clustering

B. Dimensionality Reduction

1. Principal Component Analysis (PCA)  2. Singular Value Decomposition (SVD)

IV. Deep Learning

A. Artificial Neural Networks

1. Feedforward Neural Networks  2. Convolutional Neural Networks (CNN)  3. Recurrent Neural Networks (RNN)

B. Optimization Techniques

1. Stochastic Gradient Descent (SGD) 2. Backpropagation

V. Reinforcement Learning

A. Markov Decision