Machine Learning Techniques using Python
Model evaluation and optimisation, decision trees, random forests, logistic regression, SVMs, neural networks, deep learning and more.
You will learn advanced state-of-the art machine learning techniques that are in demand in industry and research.
Languages and libraries :
PREREQUISITES
Good knowledge of python, some familiarity with matrices, basic understanding of machine learning practice (as taught in Introduction to Data Science)
AUDIENCE
Individuals who wish to take their data science skills further and learn state-of-the-art techniques in this constantly evolving field.
Get in touch with us to learn about the course
DAY ONE
RANDOM FORESTS, LOGISTIC REGRESSION, SUPPORT VECTOR MACHINES (SVMS)
Session 1
Introduction to Machine Learning
- Overview of Machine Learning
- Supervised vs. Unsupervised Learning
- Industrial Applications
Session 2
Random Forests
- Decision Trees
- Ensemble models and Random Forests
- Overfitting, validation and the bias-variance trade-off
- Hyperparameter tuning, grid search and model selection
Session 3
Support Vector Classifiers
- Linear SVCs
- The kernel trick and non-linear SVCs
DAY TWO
NEURAL NETWORKS AND DEEP LEARNING
Session 1
Neural Networks
- Overview of modern applications of Neural Networks
- The Perceptron
- Structure of general neural networks
- Training of Neural Networks
Session 2
Deep Learning
- Motivation and architecture
- Real-world examples
- Convolutional Neural Networks
- Impact and limitations of Deep Learning
CONTACT US
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.
We look forward to speaking with you.
Dr. Raoul-Gabriel Urma
Director