Course Structure

Unit 1: Introduction to Powerful Communication

• Understand the importance of public speaking and creative writing as distinct yet complementary forms of communication.

Unit 2: Public Speaking Essentials

• Learn how to deliver messages clearly and effectively to an audience.
• Develop key skills such as:

1.    Vocal control
2.    Confident body language
3.    Audience engagement

• Focus on structure, clarity, and persuasion in speech delivery.

Unit 3: Creative Writing Foundations

• Explore the expressive side of communication through imaginative language.
• Practice forms such as:

1.  Fiction
2.  Poetry
3.  Memoirs

• Understand the use of:

1.   Narrative techniques
2.   Character development
3.   Vivid imagery

Unit 4: The Synergy Between Speaking and Writing

• Discover how creative writing enhances public speaking through storytelling and emotional depth.
• Learn how public speaking brings written words to life through:

1.   Performance
2.   Audience connection

Unit 5: Mastering Expression

•  Combine both skill sets to improve overall communication.
• Develop the ability to communicate with greater impact and authenticity in both written and spoken forms.

Topics Covered are Introduction to Python Programming, Intermediate Python Programming, Object-Oriented Programming, and Data Structures and Algorithms 

Lesson Name: Python 

• Introduction to Python Programming
• Intermediate Python Programming
• Object-Oriented Programming
• Data Structures and Algorithms 

Lesson Schedule:  Monday to Friday

Timings: 8 pm to 9 pm (Philippine Time)

Practical Guide to Rational Numbers

Lesson Name: Rational numbers

• How to divide the pizza?
• How to divide the dough?

Lessons schedule: Monthly, 20 Classes

What you will learn: 

• Recognizing irrational expressions 
• Applying rationalisation techniques
•  Solving algebraic problems with square roots and surds 
• Avoiding common mistakes and improving accuracy 
• This session is suitable for CBSE, ICSE, and State Board students.

In this course, we will explore Thillana, a vibrant and energetic part of Bharatanatyam. Students will learn:

• 3 Mai's
• 2 Jatis - Trikala Jati
• Jati
• Panchajati – 5 Jatis
• Anupalavi - Abhinaya
• Last Jati

Lesson Name: Thillana

• Nritta (Pure Movements) 
• Anupallavi with Nritya (Abhinaya)
• Panchajatis- laya (tempo)
• Tala (Rhythm)

Lessons schedule  - Tuesday
Timings- 5.30 pm- 6.30 pm

Having a strong background in the fundamentals of AI/ML is crucial for building real-world applications that automate and optimize your lifestyle. Artificial Intelligence and Machine Learning precisely describe how to train our application to learn from the data, analyze it, and gain decisive abilities. 

Lesson 1: Introduction to Artificial Intelligence and Machine Learning

• What is AI? What is ML? Differences between AI, ML, DL, and Data Science
• Real-world applications
• History and evolution of AI
• Types of ML: Supervised, Unsupervised, Reinforcement Learning
• Tools setup: Python, Jupyter Notebooks, Google Colab

Lesson 2: Python for AI/ML

• Python basics recap: variables, data types, functions, control flow
• Libraries for ML: NumPy, pandas, matplotlib
• Hands-on with data loading, preprocessing, and visualization

Lesson 3: Mathematics for ML - Part 1

• Linear Algebra essentials: vectors, matrices, dot product
• Applications in ML
• Numpy-based matrix operations

Lesson 4: Mathematics for ML - Part 2

• Probability and Statistics basics
• Mean, median, variance, standard deviation
• Distributions, correlation vs. causation

Lesson 5: Supervised Learning - Regression

• Linear regression (Simple and Multiple)
• Cost function, gradient descent
• Evaluation metrics: MAE, MSE, RMSE, R² score
• Hands-on: House price prediction dataset

Lesson 6: Supervised Learning - Classification

• Logistic regression
• Decision trees
• Confusion matrix, accuracy, precision, recall, F1 score
• Hands-on: Email spam classifier

Lesson 7: Unsupervised Learning - Clustering

• K-means clustering
• Evaluation: Elbow method, silhouette score
• Hands-on: Customer segmentation

Lesson 8: Unsupervised Learning - Dimensionality Reduction

• Principal Component Analysis (PCA)
• Feature selection vs. extraction
• Hands-on: Visualizing high-dimensional data

Lesson 9: Model Evaluation and Tuning

• Train/test/validation split
• Cross-validation
• Hyperparameter tuning (Grid Search, Random Search)
• Avoiding overfitting/underfitting

Lesson 10: Introduction to Deep Learning

• What is Deep Learning? Neural Networks
• Perceptron, forward/backward propagation
• Brief intro to TensorFlow/Keras or PyTorch

Lesson 11: Capstone Project Work

• Students form groups or work individually
• Choose a dataset, define a problem, build and evaluate a model
• Instructor mentorship

Lesson 12: Project Presentations + Wrap-Up

• Students present their projects
• Peer review
• Course recap, next steps, career guidance, resources