Machine Learning Techniques using Python
Model evaluation and optimisation, decision trees, random forests, logistic regression, SVMs, neural networks, deep learning and more.
Next event: 17 Jun - 18 Jun 2017 in Cambridge
two days Course
What you will learn
You will learn advanced state-of-the art machine learning techniques that are in demand in industry and research.
- Model evaluation and optimisation (grid search)
- Decision trees and random forests
- Logistic regression
- SVMs (linear SVMs, kernel trick, nonlinear SVMs)
- Neural networks
- Deep learning (local and cloud-based)
Languages and libraries
- Python programming language
- Numpy and pandas for data manipulation
- Scikit-learn for machine learning algorithms
- Keras for neural networks and deep learning
Acquire specialised Natural Language Processing skills at our Text Mining and Natural Language Processing with Python bootcamp.
Learn how to make quantitative predictions with our Forecasting and Regression course.
Prerequisites: Good knowledge of python, some familiarity with matrices, basic understanding of machine learning practice (as taught in Introduction to Data Science)
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
- Drinks with fellow participants and lecturers
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
Continuous learning project
Our continuous learning project comprises a real-world problem and data set to complete in your own time, and practice using the course material and techniques covered during the bootcamp. The package includes model notebook answers, with a detailed explanation of the solution and problem-solving process.Price: £100 extra