📄️Introduction to Data Science and ML
Data science extracts insight from data; machine learning (ML) is the part that learns patterns from data to make predictions or decisions without being…
📄️NumPy and pandas
NumPy provides the ndarray: a typed, fixed-size, vectorised array.
📄️Data Cleaning and Exploratory Analysis
Real data is messy. Cleaning and exploring it usually takes most of the time in a project.
📄️Supervised Learning
Predict a number as a weighted sum of features: y = w1x1 + ... + b. Training minimises the mean squared error** (MSE).
📄️Model Evaluation Metrics
Choose the metric that matches the cost of mistakes, not just the default.
📄️Unsupervised Learning and Neural Networks
Group points into k clusters by repeatedly assigning each point to the nearest centroid and recomputing centroids.