**Artificial Intelligence with Python**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 6.5 Hours | 4.22 GB

eLearning | Skill level: All Levels

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

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

**+ Table of Contents**

**Random Forest and Extremely Random Forest**

1 Random Forest and Extremely Random Forest

**Class Imbalance and Grid Search**

2 Dealing with Class Imbalance

3 Grid Search

**Adaboost Regressor**

4 Adaboost Regressor

5 Predicting Traffic Using Extremely Random Forest Regressor

6 Traffic Prediction

**Detecting patterns with Unsupervised Learning**

7 Detecting patterns with Unsupervised Learning

8 Clustering

9 Clustering Meanshift

10 Clustering Meanshift Continues

**Affinity Propagation Model**

11 Affinity Propagation Model

12 Affinity Propagation Model Continues

**Clustering Quality**

13 Clustering Quality

14 Program of Clustering Quality

**Gaussian Mixture Model**

15 Gaussian Mixture Model

16 Program of Gaussian Mixture Model

**Classifiers**

17 Classification in Artificial Intelligence

18 Processing Data

19 Logistic Regression Classifier

20 Logistic Regression Classifier Example Using Python

21 Naive Bayes Classifier and its Examples

22 Confusion Matrix

23 Example os Confusion Matrix

24 Support Vector Machines Classifier(SVM)

25 SVM Classifier Examples

**Logic Programming**

26 Concept of Logic Programming

27 Matching the Mathematical Expression

28 Parsing Family Tree and its Example

29 Analyzing Geography Logic Programming

30 Puzzle Solver and its Example

**Heuristic Search**

31 What is Heuristic Search

32 Local Search Technique

33 Constraint Satisfaction Problem

34 Region Coloring Problem

35 Building Maze

36 Puzzle Solver

**Natural Language Processing**

37 Natural Language Processing

38 Segmentation Example Continues

39 Information Extraction

40 Tag Patterns

41 Chunking

42 Representation of Chunks

43 Chinking

44 Chunking wirh Regular Expression

45 Named Entity Recognition

46 Trees

47 Context Free Grammar

48 Examine Text Using NLTK

49 Recursive Descent Parsing

50 Raw Text Accessing (Tokenization)

51 NLP Pipeline and Its Example

52 Regular Expression with NLTK

53 Stemming

54 Lemmatization

55 Segmentation

56 Segmentation Example

**Introduction**

57 Recursive Descent Parsing Continues

58 Introduction to Predictive Analysis

59 Shift Reduce Parsing

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