Artificial Intelligence with Python

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

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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