Machine Learning Techniques using Python

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

Next Date: 29 Apr - 30 Apr 2017


Level

intermediate

Location

London and Cambridge

Duration

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

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.


Prerequisites

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
  • 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

Logistic Regression

Session 4

Support Vector Classifiers

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

Evening

Social

  • Drinks with fellow participants and lecturers

Day 2

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

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

Highlights

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


Book your ticket

Event:
Location:
THECUBE - Studio 5 , 155 COMMERCIAL STREET, E1 6BJ, London (London)
Ticket:
Ticket includes course materials, code resources, lunch and networking drinks.
Location:
Eagle Labs Incubator Cambridge - 28 Chesterton Road, CB4 3AZ, Cambridge (Cambridge)
Ticket:
Ticket includes course materials, code resources, lunch and networking drinks.

Registration opening soon

The booking for this event is not open yet; subscribe to our mailing list to be notified when it is:
Alternatively if you would like to receive an invoice to secure your place in advance, email us at contact@cambridgespark.com

In-house Training

Get in touch to discuss your requirements by emailing contact@cambridgespark.com 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.