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This course is a comprehensive understanding of AI concepts and its application using Python and iPython.

What you will learn

Learn What is Artificial Intelligence, Intelligence and Applications of AI.

Learn Problem solving using AI, AI search algorithms, Informed (Heuristic) Search Strategies.

Learn Local Search Algorithms, Learning System, and Common Sense

Learn Genetic algorithms, Expert Systems, and Scikit-learn module

Description

Artificial intelligence is the simulation of human intelligence through machines and mostly through computer systems. Artificial intelligence is a sub field of computer. It enables computers to do things which are normally done by human beings. This course is a comprehensive understanding of AI concepts and its application using Python and iPython.

The training will include the following;

  • What is Artificial Intelligence?
  • Intelligence
  • Applications of AI
  • Problem solving
  • AI search algorithms
  • Informed (Heuristic) Search Strategies
  • Local Search Algorithms
  • Learning System
  • Common Sense
  • Genetic algorithms
  • Expert Systems
  • Scikit-learn module

What is Artificial Intelligence?

The first idea of artificial intelligence was given by scientist Mr. Alan Turing around the time of the second world war. He suggested building a machine that can mimic the understanding of human intelligence and act like a human.


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Artificial Intelligence today is used in all fields of work specifically banking, insurance, manufacturing, retail, logistics and so on. Its application in medical diagnosis, robots, remote sensing, etc. is a high state of the art.

AI as a subject includes the use of computer science, mathematics, statistics and domain expertise.

AI has great advantages and so of them are mentioned below:

  • It provides greater precision and accuracy on detection and prediction
  • Robots trained on AI can be used to do the works which are difficult for us
  • AI has created newer technological breakthroughs in our life
  • Fraudulent activities such as credit card transactions have become easier with AI technologies
  • AI can be used in time-consuming tasks and it can save a lot of time by becoming more efficient.

You will be able to build the following as a practical project: –

  • Classifiers of various types
  • Logic Programming based optimizers
  • Heuristic Search performed on NP-complete problems
  • Natural Language Processing on text data
  • Machine Learning in general for several kinds of data
  • Logic and reasoning for model evaluation and interpretation
  • Rule-based Programming for business use cases
  • Decision Making based on AI and ML
  • Stochastic methods such as time series and HMM
English
language

Content

Introduction

Introduction to Predictive Analysis
Random Forest and Extremely Random Forest

Class Imbalance and Grid Search

Dealing with Class Imbalance
Grid Search

Adaboost Regressor

Adaboost Regressor
Predicting Traffic Using Extremely Random Forest Regressor
Traffic Prediction

Detecting patterns with Unsupervised Learning

Detecting patterns with Unsupervised Learning
Clustering
Clustering Meanshift
Clustering Meanshift Continues

Affinity Propagation Model

Affinity Propagation Model
Affinity Propagation Model Continues

Clustering Quality

Clustering Quality
Program of Clustering Quality

Gaussian Mixture Model

Gaussian Mixture Model
Program of Gaussian Mixture Model

Classifiers

Classification in Artificial Intelligence
Processing Data
Logistic Regression Classifier
Logistic Regression Classifier Example Using Python
Naive Bayes Classifier and its Examples
Confusion Matrix
Example os Confusion Matrix
Support Vector Machines Classifier(SVM)
SVM Classifier Examples

Logic Programming

Concept of Logic Programming
Matching the Mathematical Expression
Parsing Family Tree and its Example
Analyzing Geography Logic Programming
Puzzle Solver and its Example

Heuristic Search

What is Heuristic Search
Local Search Technique
Constraint Satisfaction Problem
Region Coloring Problem
Building Maze
Puzzle Solver

Natural Language Processing

Natural Language Processing
Examine Text Using NLTK
Raw Text Accessing (Tokenization)
NLP Pipeline and Its Example
Regular Expression with NLTK
Stemming
Lemmatization
Segmentation
Segmentation Example
Segmentation Example Continues
Information Extraction
Tag Patterns
Chunking
Representation of Chunks
Chinking
Chunking wirh Regular Expression
Named Entity Recognition
Trees
Context Free Grammar
Recursive Descent Parsing
Recursive Descent Parsing Continues
Shift Reduce Parsing