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Introduction to Machine Learning: One-Day Course

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This is a one-day machine learning introductory course for beginners. The course covers the basics of supervised and unsupervised learning, including regression, classification, clustering, dimensinality reduction and anomaly detection. It also includes hands-on exercises and examples using popular Machine Learning (ML) libraries like Scikit-learn.

The presentation is used to guide the instructor through the course, providing a structured outline of the topics to be covered.

Machine Learning

Image created by using ChatGPT

Table of Contents

1. Introduction to Machine Learning

  • What is AI and ML?
    • AI: The ability of machines to simulate intelligent behavior.
    • ML: A branch of AI where models are trained to learn from data and improve over time.
  • Applications:
    • ChatGPT, Netflix recommendations, fraud detection, self-driving cars, etc.
  • Types of ML:
    • Supervised Learning, Unsupervised Learning, Reinforcement Learning.
  • Key Terminology:
    • Dataset, Features, Labels, Model, Training, Testing, Hyperparameters, Overfitting, Underfitting.

Highlights

  • Comparison between supervised and unsupervised learning using Linear Regression and K-Means examples.
  • Basic visualizations of regression and clustering tasks.

Related notebook: Introduction to Machine Learning

2. Understanding the Machine Learning Workflow

Steps in the Workflow

  1. Define the Problem: Set objectives (e.g., regression, classification).
  2. Collect and Clean Data: Handle missing values, duplicates, outliers.
  3. Explore and Visualize Data: Use summary statistics and visual tools like histograms and scatterplots.
  4. Feature Engineering:
    • Selection: Remove irrelevant features.
    • Transformation: Normalize and encode data.
    • Creation: Generate new features.
  5. Split Data: Divide into training, validation, and test sets.
  6. Choose and Train a Model: Select an algorithm based on the task.
  7. Evaluate the Model: Use metrics like RMSE, Accuracy, and Silhouette Score.
  8. Hyperparameter Optimization: Use GridSearchCV or RandomizedSearchCV for fine-tuning.

Highlights

  • End-to-end example of an ML pipeline using Scikit-learn.
  • Visualization of preprocessing and evaluation results.

Related notebook: Machine Learning Workflow

3. Supervised Learning

3.1 Regression

  • Goal: Predict continuous outputs (e.g., house prices, temperature).
  • Common Algorithms:
    • Linear Regression, Polynomial Regression, Ridge, and Lasso.
  • Evaluation Metrics:
    • MAE, MSE, RMSE, ( R^2 ).

Highlights

  • Hands-on example of Linear Regression with visualization of results.
  • Analysis of regression coefficients.

Related notebook: Supervised Learning - Regression

3.2 Classification

  • Goal: Predict discrete categories (e.g., spam detection, disease diagnosis).
  • Types:
    • Binary, Multi-Class, Multi-Label Classification.
  • Evaluation Metrics:
    • Accuracy, Precision, Recall, F1-Score, Confusion Matrix.

Highlights

  • Logistic Regression example for binary classification.
  • Hands-on exercise with Random Forest Classifier.
  • Visualization of confusion matrix results.

Related notebook: Supervised Learning - Classification

4. Unsupervised Learning

4.1 Clustering

  • Goal: Group data points into clusters based on similarity without labels.
  • Types:
    • Partition-Based: K-Means.
    • Hierarchical: Agglomerative Clustering.
    • Density-Based: DBSCAN.
  • Evaluation Metrics:
    • Silhouette Score, Inertia, Visualization.

Highlights

  • K-Means Clustering example with synthetic data.
  • Visualizing clusters and centroids.

Related notebook: Unsupervised Learning - Clustering

4.2 Other Unsupervised Learning Techniques

  • Dimensionality Reduction:
    • Reduces input features while preserving patterns.
    • Example: PCA (Principal Component Analysis).
  • Anomaly Detection:
    • Identifies outliers or unusual patterns.
    • Examples: Isolation Forest, Z-scores.

Highlights

  • PCA visualization of high-dimensional data projected into 2D.
  • Hands-on example of Isolation Forest for anomaly detection.

Related notebook: Unsupervised Learning - Other Techniques

5. In-Class Assignment

  • Objective:

    • Develop a classification model using a dataset of your choice.
    • Save the model as a pickle file.

  • Steps:

    • Preprocess the data (handle missing values, encode categorical variables).
    • Train, evaluate and optimize the model.
    • Submit the pickle file of the trained model.

    Related notebook: In-Class Assignment

Using with GitHub Codespaces

You can run this repository directly in GitHub Codespaces without needing to set up anything locally.

Steps:

  1. Click the Code button in the repository.
  2. Select Open with Codespaces.
  3. If you don’t see the option, click Create codespace on main.

Once the Codespace environment loads:

  • All dependencies (e.g., Python packages) specified in requirements.txt will be automatically installed.
  • You can open and run the Jupyter Notebooks directly in the Codespace.

Acknowledgements

Thanks to Leon Boschman for contributing his ideas, slides and feedback to this course material.