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.
Exploratory Data Analysis Fundamentals
Handling Missing and Inconsistent Data
Feature Selection and Basic Filtering
Project Dataset Initialization
Mechanics of Linear Regression
Mechanics of Classification
Loss Functions and Model Objectives
Training and Testing Data Splits
Data Scaling Techniques
Encoding Categorical Variables
Building Scikit-Learn Pipelines
Training the Baseline Linear Model
Training Error vs Generalization Error
Overfitting and Underfitting
Regression Evaluation Metrics
The Confusion Matrix
Error Analysis Plots
Introduction to Cross-Validation
Diagnosing Model Weaknesses
Feature Engineering Strategies
Handling Outliers
The Bias-Variance Tradeoff
Hyperparameter Tuning Basics
Implementing Grid Search
Refining the Project Model
Evaluating Feature Importance
Advanced Feature Transformation
Regularization Techniques
Comparing Different Algorithms
Managing Model Complexity
Understanding Data Drift
Version Control for ML Experiments
Exporting Trained Models
Creating an Inference Script
Building a Simple Web Interface
Documenting ML Projects
Final Project Review
Ensemble Methods Overview
Feature Selection via Recursive Elimination
Model Interpretability Basics
Dealing with High Cardinality
Handling Multi-Collinearity
Introduction to Pipelines with Custom Transformers
Evaluating Model Calibration
Advanced Hyperparameter Search
Model Monitoring in Practice