AI Fundamentals: From Zero to Your First Model
The foundational 30-lesson course. Learn Python, build intuition for Machine Learning, and write your first AI model.
Curriculum
Lesson 1: Welcome to the World of AI
Define AI in plain language; distinguish AI hype from reality; identify 5 AI applications used daily.
Lesson 2: How Machines Learn — The Big Picture
Explain the 3 types of ML (supervised, unsupervised, reinforcement); describe the learning loop.
Lesson 3: Data — The Fuel of AI
Explain why data quality matters more than algorithm choice; define features and labels.
Lesson 4: AI in the Real World — Use Cases & Ethics Preview
Identify industries transformed by AI; recognize potential harms of AI; articulate why ethical AI matters.
But what is a neural network?
A beautifully animated visual introduction to how neural networks actually work — the foundation of modern AI.
Lesson 5: Your First Lines of Python
Write and run Python in the browser; use variables, print(), and basic arithmetic.
Lesson 6: Making Decisions — Conditionals
Use if, elif, else statements; understand comparison and logical operators.
Lesson 7: Collections — Lists and Loops
Create and manipulate lists; use for and while loops; iterate over data collections.
Lesson 8: Dictionaries and Data Structures
Create and use dictionaries; represent structured data in Python.
Lesson 9: Functions — Writing Reusable Code
Define and call functions; use parameters and return values; understand scope.
Lesson 10: NumPy — Math at the Speed of AI
Create and manipulate NumPy arrays; perform vectorized operations.
Lesson 11: Data Visualization with Matplotlib
Create plots and charts; visualize data distributions and relationships.
Lesson 12: Working with Real Data — Pandas Essentials
Load CSV data into a DataFrame; explore, select, filter, and clean data.
Python in 100 Seconds
Lightning-fast overview of Python's syntax, features, and why it dominates AI and data science.
Lesson 13: The ML Workflow
Describe the end-to-end ML workflow; explain train/test split rationale.
Lesson 14: Linear Regression — Predicting Numbers
Explain linear regression intuitively; fit a line to data using scikit-learn.
Lesson 15: Evaluating Regression Models
Calculate MSE, RMSE, MAE, and R2 score; compare model performance.
Lesson 16: Classification — Predicting Categories
Distinguish classification from regression; build a KNN classifier.
Lesson 17: Evaluating Classification Models
Calculate accuracy, precision, recall, F1; build a confusion matrix.
Lesson 18: Decision Trees — AI That Explains Itself
Explain how decision trees make predictions; visualize a decision tree.
Lesson 19: Random Forests — Wisdom of the Crowd
Explain ensemble learning; tune n_estimators; compare performance.
Lesson 20: Feature Engineering — Making Data Model-Ready
Encode categorical variables; scale numerical features; create new features.
Lesson 21: Train, Validate, Test — Doing It Right
Explain validation sets; implement cross-validation; detect overfitting.
Lesson 22: Hyperparameter Tuning — Finding the Sweet Spot
Distinguish parameters from hyperparameters; use grid search.
Gradient Descent, Step-by-Step
Crystal-clear walkthrough of gradient descent — the core optimization algorithm behind machine learning.
Lesson 23: Text Data & NLP Basics
Tokenize text; create a bag-of-words representation; perform text preprocessing.
Lesson 24: Sentiment Analysis — Your First NLP Model
Build a text classifier; apply the full ML pipeline to text data.
Lesson 25: Introduction to Neural Networks
Explain neurons and layers; understand forward propagation.
Lesson 26: Project Workshop — Problem Definition & Data
Define the final project problem; load and thoroughly explore the dataset.
Lesson 27: Project Workshop — Feature Engineering & Baseline
Preprocess text data for modeling; build and evaluate a baseline model.
Lesson 28: Project Workshop — Model Comparison & Tuning
Compare multiple models systematically; tune hyperparameters.
Lesson 29: Project Workshop — Interpretation & Presentation
Interpret model predictions; visualize results effectively.
Lesson 30: What's Next — Your AI Learning Roadmap
Summarize learned concepts; identify areas for further study.
The spelled-out intro to neural networks and backpropagation
Build a neural network completely from scratch in Python with the former Tesla AI Director.
Course Quiz
5 questions · ~5 min · Pass 4/5 to unlock your badge