Intrested to Empower Your School teachers with Advance Trainings?
Introduction
Effective teacher training enhances instructional methods, enabling educators to integrate innovative technologies into their lessons seamlessly. This not only boosts their confidence but also directly impacts student learning outcomes. When teachers are well-prepared, they inspire curiosity and creativity, encouraging students to explore and experiment with new concepts.

Training Format

Workshops

Hands-On Sessions

Online Courses
Teacher Training Syllabus
Outcomes
- Understand the importance of AI and its applications.
- Gain basic knowledge of programming.
- Develop an understanding of projects and their implementation.
- Learn Python programming language and understand its code.
- Acquire knowledge of Artificial Intelligence, Machine Learning, and Computer Vision.
- (Bonus) Apply AI applications to save time and effort (using tools like ChatGPT, Copilot, Bard, and Gemini
Course Structure
- Introduction to Programming and Python Basics (Days 1-8)
- Advance Artificial Intelligence for Data Science (Days 9-15)
Bonus Days (Optional):
- Day 1: Chat GPT and Prompt Engineering
- Day 2: Copilot and its Use
Detailed Course Outline
Introduction to Programming and Python Basics (Days 1-8)
Day 1: Introduction to Programming and Python Basics
- Terminology and Importance
- Application Requirements
- Hands-On Activity: Writing a Simple Python Program
Day 2: Python Syntax
- Syntax Rules
Day 3: Conditional Statements
- if, else, and elif statements
Day 4: Loops
- for and while loops
- Coding Exercise: Checking for Prime Numbers
Day 5: In-Built Data Structures
- Lists and Tuples
- Sets and Dictionaries
Day 6: In-Built Data Structures (Continued)
- Sets (Operations and Use)
- Dictionaries (Operations and Use)
Day 7: Functions and Data Structures
- Functions: Definition, Parameters, and Return Values
- Debugging Techniques
- Project Idea: Developing a Simple Contact Book Application
Day 8: Intermediate Python Concepts
- Keyword Arguments
- List Comprehensions
- Lambda Expressions
- Class Inheritance (Introduction to OOP)
- Project Challenge: Implementing a Basic OOP Structure
Advance Artificial Intelligence for Data Science (Days 9-15)
Day 9: Tkinter (GUI Development)
- Introduction to Tkinter
- Layout Management: Grid and Pack Layouts
- Practical: Building a Simple GUI
Day 10: Exception Handling and File Handling
- Exception Handling: try, except, and finally blocks
- File Handling: Reading from and Writing to Files
- Practical: Handling Exceptions and Reading/Writing Files
Day 11: Introduction to Python API Development
- What is an API?
- Making API Requests in Python
- Practical: Extracting Stock Price using Alpha Vantage API
Day 12: Machine Learning Basics (Linear Regression)
- Introduction to Machine Learning (Supervised Learning)
- Linear Regression: Fitting a Line to Data Points
- Practical: Linear Regression Implementation (using scikit-learn)
- Project 4: House Price Prediction
Day 13: Computer Vision
- Introduction to Computer Vision and Image Processing Techniques
- Practical: Face Detection using OpenCV
Day 14: Natural Language Processing (NLP)
- Introduction to NLP: Tokenization, Stemming, and Sentiment Analysis
- Text Analysis with NLTK
Day 15: Overall Learning Based Projects
- Project 1: Weather Forecast App
- Project 2: Random Quote Generator
Bonus Days (Optional)
- Introduction to ChatGPT and Prompt Engineering
- Hands-On Activity: Interactive Prompt Creation
- Practical Examples: Time-Saving Applications
Note: This syllabus is a guide and may be subject to change based on the instructor’s discretion and participants’ needs.
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