### is a way of making a computer, a computer-controlled robot, or a software think intelligently, in a similar manner the

### 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

**Chapter 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 a 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