Advanced Machine Learning Techniques in Python

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

Next Date: 04 Mar - 05 Mar 2017




London and 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
  • plotly for interactive visualisations
  • theano and keras for neural networks and deep learning

Progression paths

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.


Audience: Those who wish to take their data science skills further and learn state-of-the-art techniques in this constantly evolving field.

Prerequisites: Good knowledge of python, some familiarity with matrices, basic understanding of machine learning practice (as taught in Introduction to Data Science)

Day 1

Random Forests, Logistic Regression, Support Vector Machines (SVMs).

Session 1

Introduction to Machine Learning

  • Overview of Machine Learning
  • Supervised vs. Unsupervised Learning
  • Classification vs. Regression
  • Real-world 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

Logistic Regression

Session 4

Support Vector Machines

  • Linear SVMs
  • The kernel trick and non-linear SVMs



  • Drinks with fellow participants and lecturers

Day 2

Neural networks, deep learning.

Session 1

Neural networks

  • Biological inspiration and architecture
  • Network topologies
  • Learning algorithms and cost functions

Session 2

Deep Learning

  • Motivation and architecture
  • Real-world examples
  • Impact and limitations of Deep Learning

Session 3

Expert talks

  • Using deep learning to build intelligent applications
  • Pattern recognition and recommendation
  • Deep Learning in the Cloud with AWS

Session 4

Guest Speaker

  • State-of-the art application of Machine Learning

Machine Learning Practitioner Certificate

This certification acknowledges that you have successfully acquired the skills taught at the Cambridge Coding Advanced Machine Learning Bootcamp and that you are able to apply them independently.

To attain the certificate, you will be required to complete a project-based assessment after the bootcamp which you will be able to include in your own portfolio of work.

Once completed, you will receive detailed feedback on your code, problem-solving approach, and methodology, providing you with invaluable guidance on how to develop as a data scientist.

Price: £100 extra


Check out video highlights, photos and interviews from our previous bootcamps.

Book your ticket

Next Date: 04 Mar - 05 Mar 2017

Location: Eagle Labs Incubator Cambridge - 28 Chesterton Road, CB4 3AZ, Cambridge (Cambridge)

Ticket includes online course materials, code resources, lunch and networking drinks

In-house Training

Get in touch to discuss your requirements by emailing or by completing our contact form.

We can deliver this course as a private training at your office during week days.

We can also design a bespoke curriculum matching your specific training objectives.