A free, hands-on series of Google Colab notebooks that take you from the fundamentals of machine learning to applied network-security use cases. Open any notebook in your browser, run it, and experiment. No setup required.
Your first steps in machine learning: the workflow, tools and core ideas.
Cleaning, encoding, scaling and preparing data so your models can learn from it.
Predicting startup success with multiple linear regression, end to end.
Building and visualising a logistic regression classifier and its decision boundary.
Training, tuning and interpreting decision-tree classifiers.
Your first neural network: layers, activations, training and evaluation.
Applying ML to spot anomalies in network-traffic data, a core security use case.
Reducing and visualising high-dimensional data with PCA, LDA and t-SNE.
Constructing a deeper ANN from scratch and training it on real data.
An AI model that distinguishes normal from malicious network traffic.
I run these tutorials as live, hands-on workshops with datasets, exercises and mentorship tailored to your group.
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