AI/ML Foundations: Core Concepts & First Models
Understand machine learning from the ground up and train your first models.
What you'll learn
Explain core ML concepts, prepare data with pandas and numpy, and train, evaluate, and improve basic supervised models with scikit-learn.
Who it's for
Developers and analysts new to AI/ML.
Prerequisites
Basic Python and high-school math.
Machine learning from first principles — concepts, data, and your first trained models.
Curriculum
- 1
The Machine Learning Workflow
4 min - 2
Setting Up the Python ML Environment
4 min - 3
Introduction to NumPy for Data Handling
4 min - 4
Loading and Inspecting Datasets with Pandas
3 min - 5
Exploratory Data Analysis Fundamentals
3 min - 6
Handling Missing and Inconsistent Data
3 min - 7
Feature Selection and Basic Filtering
3 min - 8
Project Dataset Initialization
3 min - 9
Mechanics of Linear Regression
4 min - 10
Mechanics of Classification
4 min - 11
Loss Functions and Model Objectives
4 min - 12
Training and Testing Data Splits
3 min - 13
Data Scaling Techniques
4 min - 14
Encoding Categorical Variables
3 min - 15
Building Scikit-Learn Pipelines
4 min - 16
Training the Baseline Linear Model
3 min - 17
Training Error vs Generalization Error
4 min - 18
Overfitting and Underfitting
4 min - 19
Regression Evaluation Metrics
4 min - 20
The Confusion Matrix
3 min - 21
Error Analysis Plots
4 min - 22
Introduction to Cross-Validation
4 min - 23
Diagnosing Model Weaknesses
3 min - 24
Feature Engineering Strategies
4 min - 25
Handling Outliers
3 min - 26
The Bias-Variance Tradeoff
3 min - 27
Hyperparameter Tuning Basics
4 min - 28
Implementing Grid Search
3 min - 29
Refining the Project Model
3 min - 30
Evaluating Feature Importance
3 min - 31
Advanced Feature Transformation
3 min - 32
Regularization Techniques
3 min - 33
Comparing Different Algorithms
3 min - 34
Managing Model Complexity
4 min - 35
Understanding Data Drift
4 min - 36
Version Control for ML Experiments
3 min - 37
Exporting Trained Models
3 min - 38
Creating an Inference Script
3 min - 39
Building a Simple Web Interface
3 min - 40
Documenting ML Projects
4 min - 41
Final Project Review
4 min - 42
Ensemble Methods Overview
4 min - 43
Feature Selection via Recursive Elimination
3 min - 44
Model Interpretability Basics
4 min - 45
Dealing with High Cardinality
3 min - 46
Handling Multi-Collinearity
4 min - 47
Introduction to Pipelines with Custom Transformers
3 min - 48
Evaluating Model Calibration
4 min - 49
Advanced Hyperparameter Search
3 min - 50
Model Monitoring in Practice
4 min