A comprehensive tour of machine learning algorithms. Understand the maths, implement from scratch in Python, and apply Scikit-learn/TensorFlow.
Course Contents
4 sections • 14 lectures
- Simple and Multiple Linear Regression
- Polynomial Regression
- Ridge and Lasso Regularisation
- Logistic Regression
- K-Nearest Neighbours
- Support Vector Machines
- Decision Trees and Random Forests
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis
- Neural Network Architecture
- Forward and Backward Propagation
- Keras and TensorFlow Basics
- Convolutional Neural Networks
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