Unique Online Classes & Pre-Recorded Courses

Are you looking for a fun and engaging way for your child to learn coding? I’m a Senior Coding Coach with over 5 years of experience in teaching students through hands-on, interactive lessons.

I specialize in such as HTML, CSS, JavaScript, and Python (Basics), making complex concepts easy to understand. My teaching style includes fun activities, real-world projects, and creative problem-solving, tailored to each learner’s pace and interest.

Students will also get exposure to advanced and exciting tools like TinkerCAD (3D design), Google Dialogflow (AI chatbots), and many more platforms that spark innovation and curiosity.

Whether your child is a beginner or looking to deepen their knowledge, I ensure every session is impactful, enjoyable, and packed with learning.

Let’s make coding a fun adventure!

Learn to code the easy and exciting way with, and also dive into the world of Python, HTML, CSS, and JavaScript, all explained in a simple, kid-friendly manner. Whether you're a beginner or just curious, you'll see how coding can be fun and creative. Let's build games, websites, and more together! 

Learn from the basics to advanced.

Learn Basic Python.

Curriculum Details 

🔹Data Types

🔹Variables and Assignment

🔹Operators 

🔹Control Structures

🔹Basic Data Structures

🔹Functions

🔹Modules and Packages

🔹Input and Output

🔹File Handling

🔹Object-Oriented Programming (OOP) Basics

🔹Libraries

• NumPy
• Pandas
• Matplotlib/Seaborn

🔹Debugging and Error Handling

Lessons schedule: Mon and Tue (6 pm to 7 pm)

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)

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