Intro to Machine Learning

An introductory practical machine learning course using Python with a strong application focus.

Time (timezone converted to UTC)

Sun, 8:00 – 9:00 PM UTC

Dates (mm/dd)

7/7, 7/14, 7/21, 7/28, 8/4, 8/11



Ages (recommended)


An introductory practical machine learning course with a strong application focus. Students will be expected to come into the course with a strong basic foundation in Python. We will use scikit-learn throughout the beginning half of the course and transition to PyTorch, a widely used open-source machine learning framework in the 2nd half.

Each hour long class will contain approximately 20-30m of lecture and then approx. 30m of hands-on lab time where students will code along with the instructor(s). The class will cover elements of both unsupervised and supervised learning.


1What is machine learning: supervised learning, regression vs classification problems
2Linear & logistic regression, plotting & making graphs using matplotlib
3Naive Bayesian classifiers, support vector machines (SVM), decision trees (theory)
4Foundations of neural networks: perceptron model, gradient descent, ReLU, etc.
5Implementing a basic neural network in PyTorch (plus some basic model validation and saving/loading weights)
6Introduction to natural language processing using Transformers (Hugging Face)

Kids for Code

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