Live online Machine Learning classes for kids

Class 1: What is Machine Learning
Project: The Learning Toy – I’ll show you how a toy can become smarter without us giving it strict rules. Just like you learn by trying again and again, this toy learns from examples. By the end, you’ll understand how machines “learn” instead of being told everything.

Class 2: Types of data (structured, unstructured)
Project: Treasure Chest of Clues – Imagine opening a treasure chest filled with numbers, pictures, words, and notes. I’ll help you understand which clues are neat and easy for a machine, and which ones need extra thinking—just like sorting toys before playing.

Class 3: Features and labels
Project: Guess My Animal – We’ll play a fun guessing game where I teach the computer using clues like color, size, and sound. You’ll decide what hints to give and what answer the computer should guess. This makes machine learning feel like a smart guessing game.

Class 4: Dataset splitting (training, validation, testing)
Project: Practice, Test, Win! – Just like you don’t give an exam without practice, machines also need fair practice and testing. I’ll show you how we divide information so the computer doesn’t cheat and actually learns properly.

Class 5: Supervised learning
Project: You Are the Coach – In this project, you become the coach and guide the computer with correct answers. Slowly, you’ll watch it improve—just like teaching a younger sibling. I’ll connect this to things kids see daily, like game suggestions.

Class 6: Unsupervised learning
Project: Mystery Sorting Game – Here, the computer gets no answers at all. I’ll help you see how it finds patterns on its own, like sorting candies by shape or color without being told how.

Class 7: Semi-supervised learning
Project: Half-Known Puzzle – Sometimes we know answers for only a few things. I’ll show you how the computer still learns using a mix of known and unknown clues—just like solving a puzzle with missing pieces.

Class 8: Reinforcement learning
Project: Treats Make Me Smarter – You’ll train a character that learns by getting rewards and making mistakes. I’ll explain how this is exactly how kids learn games—try, fail, get better, and win.

Class 9: Regression
Project: Number Guess Wizard – We’ll teach the computer to make smart number guesses, like predicting scores or heights. I’ll make this feel like estimating how tall you’ll grow next year.

Class 10: Classification
Project: Yes or No Machine – Here, the computer learns to choose between options—this or that, yes or no. I’ll connect this to everyday decisions like sorting emails or choosing the right game level.

Class 11: Clustering
Project: Friend Group Maker – Without any instructions, the computer will form groups by itself—just like kids naturally form friend circles. I’ll show you how machines find similarities without being told.

Class 12: Dimensionality reduction
Project: Smart Backpack Packing – You’ll learn how to keep only what’s important and remove extra stuff—just like packing your school bag wisely so it’s light and useful.

Class 13: Feature engineering
Project: Clue Upgrade Lab – Here, you’ll improve the clues you give the computer so it becomes smarter. I’ll show you how better hints make learning faster and more accurate.

Class 14: Feature scaling (normalization, standardization)
Project: Fair Playground Rules – I’ll explain how we make sure no clue is too strong or too weak—just like fair rules in a playground so everyone gets an equal chance.

Class 15: Handling missing data
Project: Fix the Missing Pieces – Sometimes information is incomplete. I’ll teach you how to calmly fix missing parts so the computer doesn’t get confused—just like completing an unfinished drawing.

Class 16: Handling outliers
Project: Spot the Odd One Out – You’ll learn how to identify strange values that don’t belong, like spotting a giraffe in a group of cats, and decide what to do with them.

Class 17: Bias–variance tradeoff
Project: Too Easy or Too Hard? – I’ll help you understand why learning can be too simple or too complicated. Together, we’ll find the perfect balance—just like choosing the right difficulty level in a game.

Class 18: Overfitting, underfitting
Project: Learn Just Right – Memorizing everything is bad, guessing randomly is worse. I’ll help you build a learner that understands—not memorizes—just like a good student.

Class 19: Model selection
Project: Pick the Smartest Helper – We’ll compare different “brains” and choose the best one for the job. I’ll show you how smart choices make better results.

Class 20: Hyperparameters, hyperparameter tuning
Project: Control Knob Challenge – You’ll play with settings like speed and strength to improve performance. I’ll show how tiny changes can make a big difference.

Class 21: Loss functions, optimization techniques
Project: Fewer Mistakes Game – I’ll explain how machines count mistakes and slowly reduce them—just like improving your score every time you play.

Class 22: Gradient descent, learning rate, epochs, batch size
Project: Downhill Learning Adventure – Learning will feel like walking down a hill carefully. I’ll explain steps, speed, and repetition using a fun journey story.

Class 23: Distance measures, similarity measures
Project: Who Looks Like Me? – You’ll teach the computer how to decide who or what is similar. I’ll connect this to friend suggestions and game recommendations.

Class 24: Evaluation metrics, confusion matrix
Project: AI Score Report – Instead of just marks, we’ll look at a full report card. I’ll show you how to truly understand how well the computer is doing.

Class 25: Cross-validation
Project: Fair Testing Day – We’ll test the computer again and again to make sure it’s not lucky—just truly smart. This builds trust in learning.

Class 26: Probabilistic models, Bayesian learning
Project: Smart Guessing Game – I’ll show you how machines think in chances, like guessing if it might rain. This makes AI feel very human.

Class 27: Linear regression, logistic regression, k-NN, Naive Bayes
Project: Old But Gold Brains – You’ll meet famous learning styles that still power many apps today. I’ll explain each one with simple, fun examples.

Class 28: Decision trees, random forest
Project: Question Path Game – The computer will ask questions step by step to decide. I’ll also show how many decision makers together become stronger.

Class 29: Ensemble learning, bagging, boosting
Project: Power of Teamwork – Here, many learners work together to give better answers—just like a team winning over a solo player.

Class 30: Neural networks, activation functions, backpropagation
Project: Build a Tiny Brain – You’ll see how a simple brain learns from mistakes. I’ll connect this to how humans improve with practice.

Class 31: Deep learning, CNN, RNN, LSTM, Transformers
Project: Super Smart Machines – I’ll introduce the powerful ideas behind face recognition, voice assistants, and chatbots—explained like magic tricks with logic.

Class 32: Regularization, explainability, ethics, deployment, monitoring, concept drift
Project: Kind and Honest AI – You’ll learn how to build AI that is fair, safe, and trustworthy. I’ll explain why being responsible with technology is just as important as being smart.