Artificial intelligence 1st Edition by Pon Mahesh ISBN 935260467X 9789352604679

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Product details:

ISBN 10: 935260467X
ISBN 13: 9789352604679
Author: Pon Mahesh 

Artificial Intelligence for beginners. Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term “artificial intelligence” is often used to describe machines (or computers) that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem-solving “In this book, I’ll be covering all the domains and the concepts involved under the umbrella of artificial intelligence, and I will also be showing you a couple of use cases and practical implementations by using Python. So, there’s a lot to cover in this session.

Artificial intelligence 1st Table of contents:

Following topics are covered:
History Of AI
Demand For AI
What Is Artificial Intelligence?
AI Applications
Types Of AI
Programming Languages For AI
Introduction To Machine Learning
Need For Machine Learning
What Is Machine Learning?
Machine Learning Definitions
Machine Learning Process
Types Of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised vs Unsupervised vs Reinforcement Learning
Types Of Problems Solved Using Machine Learning
Supervised Learning Algorithms
Linear Regression
Linear Regression Demo
Logistic Regression
Decision Tree
Random Forest
Naive Bayes
K Nearest Neighbour (KNN)
Support Vector Machine (SVM)
Demo (Classification Algorithms)
Unsupervised Learning Algorithms
K-means Clustering
Demo (Unsupervised Learning)
Reinforcement Learning
Demo (Reinforcement Learning)
AI vs Machine Learning vs Deep Learning
Limitations Of Machine Learning
Introduction To Deep Learning
How Deep Learning Works?
What Is Deep Learning?
Deep Learning Use Case
Single Layer Perceptron
Multi-Layer Perceptron (ANN)
Backpropagation
Training A Neural Network
Limitations Of Feed Forward Network
Recurrent Neural Networks
Convolutional Neural Networks
Demo (Deep Learning)
Natural Language Processing
What Is Text Mining?
What Is NLP?
Applications Of NLP
Terminologies In NLP
NLP Demo
Machine Learning Masters Program

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