Attend Two Week Coding & AI Training for Teachers

According to NEP 2020, Digitalization and coding will be compulsory from next year onwards but it is important that concerned persons should be aware of this concept so that they can implement it in school properly, and can guide their students on right and wrong according to the age group.

The New and upcoming era is all about Technology and coding. Up to 20 million manufacturing jobs around the world could be replaced by robots by 2030, according to analysis firm Oxford Economics
Coding is going to compulsory from next year according to NEP 2020, so here we are with a program that develops the skills of teachers/educators.

Our Previous association

1)Techfest, IIT Bombay
2)Elan & nVision IIT Hyderabad
3)E-cell IIT Kharagpur
4)Aakar, IIT Bombay
5) Radiance,IIT Bombay
6) Rendezvous, IIT Delhi
7) Global Edu Leaders Forum

Course Content for Two Week Training Program

Module 1

• Getting started with python programming
• Installing Anaconda
• Python variables, lists, tuples and dictionaries
• Control Structure in Python
• Defining Functions in Python
• Machine Learning
• Introduction and Applications of Machine Learning
• Supervised and Unsupervised Learning

Module 2

• Classification & Regression Problem
• Clustering, Anomaly Detection
• Getting started with Linear Regression
• Mathematics behind Linear Regression
• Building Linear Model
• Gradient Descent Algorithm
• Error Correction
• Using modules and packages
• Numpy for Data computation
• Matlplotlib for Data Visualization
• Pandas for data exploration
• Using scikit-learn
• Creating linear regression models using scikit-learn
• Introduction to Artificial Intelligence
• Applications of AI & Current trends
• Different AI Techniques
• AI Agents
• PEAS Analysis
• Agent Environment Analysis
• Different Types of AI Agents

Module 3

• K Nearest Neighbour Models
• Using KNN for Data Classification
• Building Models using KNN
• Support Vector Machine – Applications and Mathematics
• Using SVM for classification
• Projects

Module 4

• Getting Started with Artificial Neural Networks
• Introduction to neurons, weights
• Activation Function
• Input Layers, Hidden Layers and Output Layers
• Single layer perceptron Model
• Multilayer Neural Network
• Back Propagation Algorithm introduction
• Programming Neural Network using Python
• Building Regression models using ANN
• Classification Examples using ANN

Registration Charges

:- Rs 499/- +GSTper participant for Early Birds
Rs 699/-+GST for others


:- 1) Certificate of participation
2) Soft copy study material, Training PPTs and /Live and contact Session (Recorded session will also available)
4)Course completion letter upon completion


:- 1) Computer system with internet
2) Laptop with window 7/8/10,i3/i5 64 bit processor with 4 Gb Ram

Who Can Attend

:- Any computer teacher /physics teacher /Maths teacher/ Science teacher can attend

Complete Coding Courses from Basic to Advanced Level

1. Phase 1:-Basic of Coding and Programing
2. Phase 2:-Advance Coding(HTML,javascript,CSS)
3. Phase 3:-Python and Artificial Intelligence (as per CBSE curriculum)
4. Phase 4:-Advance Artificial Intelligence and Data Science
1. Phase 1:-8 classes(15 days)
2. Phase 2:-20 Classes(1 month
3. Phase 3:-40 Classes(2 month)
4. Phase 4:-60 classes(3-4 month)

Benefits of attending this Program

• Every participant will get the Expert Trainee certificate on the behalf of Elan & nvision IIT Hyderabad.
• Coding is going to compulsory from next year, so this program helps teachers to understand the concept of it and in the proper implementation
• Implementation of a coding program is going to easy for the schools.
• Schools will get some free sessions for the students as well.


Certificate of Participation

All the participating candidates will be provided with a certificate of participation on behalf of our company in association with Elan & Nvision’20 IIT Hyderabad.

Appreciation letter to the Organizer/Coordinator

A Letter of Appreciation will be issued for the concerned Person for appraising their contribution and efforts to conduct training for the teachers.

Association letter to the Institutes

A letter of the association will be issued to the institute for their support provided and conduction of this grand event.

Complete Syllabus for Training

Phase 1:-Basic of Coding and Programing

Introduction to programming
History of programming languages
Programming Environment
Data Types
Logical and Arithmetical Operators
If else conditions
Numbers, Characters and Arrays
Input and Output Operations

Phase 2:-Advance Coding(html,javascript,CSS)

Basic website building with practical
HTML,CSS &Javascript Basics
Building web pages with Javascript
Below are the JS concepts that are to be covered
Javascript - Home &Overview
Javascript - Syntax&Enabling
Javascript - Placement ,Variables&Operators
Javascript - If...Else &Switch Case
Javascript - While Loop &For Loop
Javascript -
Javascript - Loop Control
Javascript - Functions ,Events & Cookies
Javascript - Page Redirect &Dialog Boxes
Javascript - Void Keyword
Javascript - Page Printing

Phase 3:-Python and Artificial Intelligence (as per CBSE curriculum)

Discuss How Python Program runs
Types and Operations in python
Statements and Syntax
Understanding Modules in Python
Module Packages
Introduction to OO Programming in python
Introduction to Exceptions
2. Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s) with TensorFlow and Keras
3. Data Visualization in Python with MatPlotLib and Seaborn
4. Transfer Learning
5. Sentiment analysis
6. Image recognition and classification
7. Regression analysis
8. K-Means Clustering
9. Principal Component Analysis
10. Train/Test and cross validation
11. Bayesian Methods
12. Decision Trees and Random Forests
13. Multiple Regression
14. Multi-Level Models
15.Support Vector Machines

Phase 4:-Advance Artificial Intelligence and Data Science

1. Introduction and Importance of Data Science
2. Statistics
3. Working on Data Mining, Data Structures, and Data Manipulation
4. Algorithms used in Machine Learning
5. Data Scientist Roles and Responsibilities
6. Data Acquisition and Data Science Life Cycle
7. Deploying Recommender Systems on Real-World Data Sets
∙ Reinforcement Learning
∙ Collaborative Filtering
∙ K-Nearest Neighbor
∙ Bias/Variance Tradeoff
∙ Ensemble Learning
∙ Term Frequency / Inverse Document Frequency
∙ Experimental Design and A/B Tests
∙ Feature Engineering
∙ Hyperparameter Tuning