Highlights
Tuition fee
4450 GBP / full
4450 GBP / full
Unknown
Tuition fee
4450 GBP / full
4450 GBP / full
Unknown
Duration
19 days
Duration
19 days
Apply date
Unknown
Unknown
Apply date
Unknown
Unknown
Start date
Unknown
Start date
Unknown
Campus location
London, United Kingdom
Campus location
London, United Kingdom
Taught in
English
Taught in
English

About

From chatbots, personalised recommendations on social media, traffic predictions and virtual personal assistants including Siri and Alexa, advances in machine learning are becoming an integral tool that help individuals navigate the modern world. London School of Economics and Political Science offers the Machine Learning in Practice programme.

Overview

Key facts

Increased adoption of such technology across the world has driven massive growth in the volume of data, requiring businesses to harness the power of machine learning to make decisions, learn about and predict customer behaviour to drive strategic advantage.

Combining the fields of engineering, statistics, mathematics and computing, machine learning is one of the leading data science methodologies revolutionising business. London School of Economics and Political Science's Machine Learning in Practice course will cover a wide range of machine learning methods, both model-based and algorithmic. Presenting the theoretical foundations of these methodologies, you will have an opportunity to apply them using real-world examples and datasets.Computer seminars that enable you to practice your programming skills and a course project give you the opportunity to explore how machine learning can be used innovatively to solve pressing business challenges such as algorithmic trading in the financial industry, predicting customer behaviour, and improving compliance and risk management. By the end of the course, you will have developed the ability to understand how machine learning can be integrated into current business models and the challenges that this poses.

Programme Structure

Courses include:

  • Supervised and unsupervised learning
  • Statistical inference and probability theory
  • Linear regression and related methods
  • Classification techniques, logistic regression and discriminant analysis
  • Tree-based methods
  • Graphical models and Markov graphs

Key information

Duration

  • Full-time
    • 19 days

Start dates & application deadlines

More details
    • Visit the website for full details.

Language

English

Credits

7 ECTS
  • 3-4 credits (US) 
  • 7.5 ECTS points (EU)

Delivered

On Campus

Campus Location

  • London, United Kingdom

What students do after studying

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Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Other requirements

General requirements

This course is designed for students from various disciplines that use data to inform decision-making. It is suitable if you want an in-depth understanding of how machine learning can be integrated into modern business.You should also consider taking this course if you are targeting a career in IT, data science, marketing, research, consulting and business management. It is equally suited if you are a professional already working in industry, government, or research organisations, looking to develop your understanding of this rapidly developing field.

Tuition Fees

Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
  • International

    Non-residents
    4450 GBP / full
    4450 GBP / full
  • Domestic

    Citizens or residents
    4450 GBP / full
    4450 GBP / full

Additional Details

Standard rate

  • One Session: £4,450
  • Two sessions: £7,800
  • Three sessions: £9,600
Student rate
  • One Session: £3,350
  • Two sessions: £5,550
  • Three sessions:£6,450

Living costs

London

United Kingdom
1137 - 2157 GBP / month

The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.

Funding

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Machine Learning in Practice
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