Machine Learning Techniques using Python

Model evaluation and optimisation, decision trees, random forests, logistic regression, SVMs, neural networks, deep learning and more.

  • Level: intermediate
  • Duration: 2-day course
  • Delivered: in-house

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


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


Prerequisites: 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.

Get in touch

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