Artificial Intelligence

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think

Artificial Intelligence in Today's World

industry products

Chatbot

Phone Unlocking with Anti-face Spoofing

Speech-Based IVRS

Autonomous Vehicle

Machine Learning

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

Reinforcement

reinforcement is a consequence applied that will strengthen an organism's future behavior whenever that behavior is preceded by a specific antecedent stimulus.

Deep learning

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

Neural Networks

Neural networks are parallel computing devices, which are basically an attempt to make a computer model of the brain.

Courses Descriptions

Python

Chaper 1: An Introduction to Python
Chapter 2: Beginning Python Basics 
2.1. The print statement
2.2. Comments
2.3. Python Data Structures & Data Types
2.4. String Operations in Python
2.5. Simple Input & Output
2.6. Simple Output Formatting
Chapter 3: Python Program Flow 
3.1. Indentation
3.2. The If statement and its’ related statement
3.3. An example with if and it’s related statement
3.4. The while loop
3.5. The for loop
3.6. The range statement
3.7. Break & Continue
3.8. Assert
3.9. Examples for looping
Chapter 4: Functions & Modules
4.1. Create your own functions
4.2. Functions Parameters
4.3. Variable Arguments
4.4. Scope of a Function
4.5. Function Documentation/Docstrings
4.6. Lambda Functions & map
4.7. An Exercise with functions
4.8. Create a Module
4.9. Standard Modules
Chapter 5: Exceptions
5.1. Errors
5.2. Exception Handling with try
5.3. Handling Multiple Exceptions
5.4. Writing your own Exceptions
Chapter 6: File Handling 
6.1. File Handling Modes
6.2. Reading Files
6.3. Writing & Appending to Files
6.4. Handling File Exceptions
6.5. The with statement
Chapter 7: Classes In Python 
7.1. New Style Classes
7.2. Creating Classes
7.3. Instance Methods
7.4. Inheritance
7.5.Polymorphism
7.6. Exception Classes & Custom Exceptions
Chapter 8: Regular Expressions 
8.1 Simple Character Matches
8.2 Special Characters
8.3 Character Classes
8.4 Quantifiers
8.5 The Dot Character
8.6 Greedy Matches
8.7 Grouping
8.8 Matching at Beginning or End
8.9Match Objects
8.10 Substituting
8.11 Splitting a String
8.12 Compiling Regular Expressions
8.13 Flags
Chapter 9: Data Structures 
9.1 List Comprehensions
9.2 Nested List Comprehensions
9.3 Dictionary Comprehensions
9.4 Functions
9.5 Default Parameters
9.6 Variable Arguments
9.7 Specialized Sorts
9.8 Iterators
9.9 Generators
9.10 The Functions any and all
9.11 The with Statement
9.12 Data Compression

Machine Learning

Installation and configuration

Data Preprocessing

Regression Techniques
Simple Linear Regression
Multiple Linear Regression
Polynomial Linear Regression
Support Vector Regression
Decision Tree Regression
Random Forest Regression
Evaluating Regression Model Performance

Classification Techniques
K-Nearest Neighbors (KNN)
Support Vector Machine (SVM)
Kernel SVM
Naïve Bayes Classification
Decision Tree Classification
Random Forest Classification
Evaluating Classification Model Performance

Natural Language Processing (NLP)
Basic of NLP
Language preprocessing Techniques
Auto summarizing the given text document

Clustering Techniques
K-Means Clustering
K-mini Batch Clustering
Hierarchical Clustering

Elbow Method

Curve Smoothening Techniques

Association Rule Learning

Reinforcement Learning

Basics of Numpy and panda

Deep Learning 
Basics/what is Deep Learning

Artificial Neural Networks

Dimension Reduction Techniques
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)

Statistics Basics
Standard Deviation
Variance
Co-Variance
T-distribution
Pearson Correlation Coefficient (PCC)/ Correlation Coefficient

Artificial Intelligence

Description:
In this course you will learn to implement mathematical ideas in machine learning. You will investigate the process of learning and understand the application of various learning algorithms.

Prerequisites:
The courses assignments and notes will use python programming language and expects a basic knowledge of python. We assume the student has completed the Machine Learning Foundations or has an equivalent fluency in mathematics and fundamentals.

course details :

Linear Models
Understand linear approximation and modelling of problems and develop linear models

Dimensionality Reduction
Use ideas from linear algebra to transform dimensions and warp space providing additional flexibility and functionality to linear models.

SVM
Develop and implement kernel based methods to develop nonlinear models to solve few complex tasks.

Nearest Neighbours, K-means, and Gaussian Mixture Models
Review pattern recognition ideas with distance and cluster based models to understand similarity measures and grouping criteria.

Naive Bayes and Decision Trees
Dive into applications of bayes theorem and the use of decision criteria when learning from data.

Search
Look at search from the perspective of graphs, trees and heuristic based optimizations.

Logic and Planning
Discover ways to encode logic and develop agents that plan actions in an environment.

Reinforcement Learning and Hidden Markov Models
Engineering agents that learn from a sequence of actions using rewards and penalties.

Q-Learning and Policy gradient
Operate in a stateful world over value and policy approximations tasks