Machine Learning Techniques using Python
Model evaluation and optimisation, decision trees, random forests, logistic regression, SVMs, neural networks, deep learning and more.
What you will learn
You will learn advanced state-of-the art machine learning techniques that are in demand in industry and research.
Languages and libraries :
Good knowledge of python, some familiarity with matrices, basic understanding of machine learning practice (as taught in Introduction to Data Science)
Individuals who wish to take their data science skills further and learn state-of-the-art techniques in this constantly evolving field.
RANDOM FORESTS, LOGISTIC REGRESSION, SUPPORT VECTOR MACHINES (SVMS)
Introduction to Machine Learning
- Overview of Machine Learning
- Supervised vs. Unsupervised Learning
- Industrial Applications
- Decision Trees
- Ensemble models and Random Forests
- Overfitting, validation and the bias-variance trade-off
- Hyperparameter tuning, grid search and model selection
Support Vector Classifiers
- Linear SVCs
- The kernel trick and non-linear SVCs
NEURAL NETWORKS AND DEEP LEARNING
- Overview of modern applications of Neural Networks
- The Perceptron
- Structure of general neural networks
- Training of Neural Networks
- Motivation and architecture
- Real-world examples
- Convolutional Neural Networks
- Impact and limitations of Deep Learning
Get in Touch
We will email you within the next 24 hours to arrange a quick call to help with any questions about the programme and recommend pre-course materials.