#### Level-1

## Introduction to AI and Machine Learning

Learn the basics of modern-time AI and represent an application of it. Discover the numerous ways to apply AI and possible developments.

#### AI using Javascript

Create intelligence with us. This section covers the overview of AI and models, from facial recognition to sound classification.

- Introduction to AI and ML
- Basics of JavaScript
- JS Library P5 - Shapes
- JS Library P5 - Colors
- AI powered Snake Game
- Introduction to ML5 & K Means Algorithm
- Image Classification
- Pose Detection

- K Nearest Neighbour Algorithm
- KNN Pose Net
- BodyPix Model and Image Segmentation
- Sound Classification and Dina Game
- Word2Vec
- Sentiment Analysis

#### Neural Network in AI

This section provides a complete understanding of Neural Networks and implementation of object and face detection in AI projects.

- Object Detection
- Object Detection Project
- Sketch RNN
- Sketch RNN Project
- Face API
- Face Detection
- Neural Networks
- Basketball Game Development

- Adding AI to the Game

## Learning Outcomes

- Learn basics and foundations of machine learning
- Gain ability to incorporate the tools required to develop ML Model
- Create your own AI powered game

### Major Project

- Image Classification Project

## Skill Benefit

- Enhanced Mental Skills
- Enhanced Creativity Skills
- Ability to use JavaScript’s p5 Library to Make Games
- Ability to use Google’s Teachable Machine to make Machine Learning Models

#### Level-2

## Mathematics and Statistics

Build a foundation for your data science skills by learning mathematics and statistics techniques and formulas to analyse the data in the proper manner.

#### Basics Mathematics and Algebra for DataScience

This section covers the basics of mathematics, algebra and equations functionalities and the usage of these equations in data science.

- Introduction to Algebra
- Solving basic Equations & Inequalities
- Linear Equations Part 1
- Linear Equations Part 2
- Functions Part 1
- Functions Part 2
- Quadratic Equations Part 1
- Quadratic Equations Part 2
- Polynomial Expressions
- Exponential and Logrithmic Functions
- Introduction to Trigonometry
- Trigonometric Functions Part 1

- Trigonometric Functions
- Series and Induction
- Vectors
- Matrices

#### Statistics and Probability

This section covers the concepts of Statistics and Probability which helps to create the algorithms and develop computing skills in Data Science.

- Introduction to Statistics
- Mean, Mode, Median
- Variance, Covariance and Coorelation
- Linear Regression and Hypothesis Testing
- Introduction to Probability
- Counting, Permutation and Combinations
- Random Variables, Sampling Distribution
- Advanced Regression

#### Calculus in Mathematics

This section covers the concept of Calculus in Mathematics which is used to analyse the data properly in Data Science.

- Introduction to Calculus
- Composite and Inverse Functions
- Complex Numbers Rational Functions
- Limits and Continuity

- Derivatives
- Integrals
- Differential Equations
- Series

### Major Project

- Quadratic Equation
- Differential Equation

### Learning Outcomes

- Master with Mathematical Problems and Mathematic Algorithms
- Apply Logical Thinking to Problem Solving in Context
- Demonstrate Skills in writing Mathematics

### Skill Benefit

- Enhance Analytical Skiils and Programming Skills
- Developed Mathematical Skills

### Foundation

#### Level 1

#### 30 Hours

- 1:1 Personalised and Customised Live Sessions
- Access to E-Learning Resources and Community
- After-Class Assignments and Quizzes
- Work on Real-Time Projects
- Course Level Completion Certificate
- 24x7 Customer Support

### Intermediate

#### Level 2

#### 38 Hours

- 1:1 Personalised and Customised Live Sessions
- Access to E-Learning Resources and Community
- After-Class Assignments and Quizzes
- Work on Real-Time Projects
- Motivational Sessions
- Course Level Completion Certificate
- 24x7 Customer Support

### Expert

#### Level 3

#### 40 Hours

- 1:1 Personalised and Customised Live Sessions
- Access to E-Learning Resources and Community
- After-Class Assignments and Quizzes
- Work on Real-Time Projects
- Personality Development Sessions
- Mindfullness Activity
- App Deployment
- 24x7 Customer Support
- Course Completion Certificate

### AI with Machine Learning and Data Science

#### 108 Hours

- Personalised Learning
- Deploy your own project and App
- Focus on Personality Development
- Focus on Extra Curriculum Activities
- Access of E-learning portal, Project Gallery and Community
- Course Completion Certificate
- Prepare for Course Certifications

#### Level-3

## Introduction to Data Science and Tools

Start your career in Data Science by learning the basic concepts of data Science.

Datascience is a vast field and involves handling data in numerous ways. Learn the techniques and implementation of data with the Datascience libraries: Numpy, Pandas, Matplotlib, and TensorFlow.

#### NumPy - Data Science Tool

This section covers the NumPy (Numerical Python) datascience tool which provides an efficient interface to store and operate on dense data buffers.

- What is NumPy?
- NumPy Arrays
- Adding, Removing and Sorting Elements
- Reshaping and Converting Arrays

- Array Operations and Broadcasting
- Indexing and Slicing
- Matrics and Generating Random Numbers
- Transposing and Reshaping a Matrix
- Working with Mathematical Formulas
- Importing and Exporting a CSV and Plotting arrays with Matplotlib

#### Pandas - Data Science Tool

This section covers the most preffered tool Pandas which is fast, powerful, flexible and easy to use open source data analysis and manipulation tool.

- What is Pandas?
- Introduction to data structures and basic functionalities
- Input Output Tools
- Indexing, Selecting data and Multi-indexing and Advanced Indexing
- Merge, Join, Concatenate, Compare Objects & Reshaping and Pivot Tables
- Working with text data and missing data
- Chart Visualization

- Table Visualization
- Computational Tools
- Time Series/ Date Functionality and Time Deltas

#### Matplotlib - Data Science Tool

This section covers the next Data Science tool Matplotlib which is a multiplatform data visualization library built on NumPy arrays.

- What is Matplotlib?
- Anatomy of a Matplotlib Figure
- Scatter Plot and Bar Plot
- Histograms and Subplots
- Customizing the Plots

#### Tensorflow - Data Science Tool

This section covers the concepts of Tensorflow tool which allows data scientists to create dataflow graphs.

- What is TensorFlow and Why use it?
- TensorFlow Variables
- TensorFlow Automatic Differentiation
- Introduction to Graphs and Functions
- Introduction to Modules, Layers and Models
- Training Loops
- Advanced AutoDiff
- Ragged Tensor

- Sparse Tensor
- Tensor Slicing
- Data Input Pipelines
- Optimization and Performance

## Learning Outcomes

- Develop in depth understanding of the key technologies in Data Science and Business Analytics
- Deep set of core competencies in multiple areas
- In-Depth Knowledge of Data Science tools

## Skill Benefit

- Enhance Critical thinking
- Programmatical thinking and increase logical building