Free Online Course on Advanced Machine Learning

The Open University (OU) and European Data Science Academy (EDSA) are offering free online course on Advanced Machine Learning. This online course explores advanced statistical machine learning.

In this course, applicants will discover where machine learning techniques are used in the data science project workflow. The course will start on March 5, 2018.

Course At Glance

Length:  4 weeks
Effort: 4 hours/week
Subject: Advanced Machine Learning
Institution: Open University (OU), European Data Science Academy (EDSA) and Future learn
Languages: English
Price: Free
Certificate Available: Yes
Session: Course starts on 25 March 2019

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Providers’ Details

The Open University (OU) is the largest academic institution in the UK and a world leader in flexible distance learning.

The European Data Science Academy (EDSA) designs curricula for data science training and data science education across the European Union (EU).

About This Course

This online course explores advanced statistical machine learning.

You will discover where machine learning techniques are used in the data science project workflow. You will then look in detail at supervised learning statistical modeling algorithms for classification and regression problems, examining how these algorithms are related, and how models generated by them can be tuned and evaluated.

You will also look at feature engineering and how to analyse sufficiency of data.

Why Take This Course?

This is a free online course. This MOOC will be offered with Video Transcripts in English.  Applicants can get a verified certificate.

Learning Outcomes

By the end of the course, you’ll be able to…

  • Explain the steps of a typical data science problem, and perform those steps identified as falling under the responsibility of a machine learning specialist.
  • Perform a range of pre-processing steps, including feature engineering and management of missing data, as well as explain the utility and importance of such methods.
  • Apply a range of advanced machine learning techniques from all major areas of machine learning (supervised, unsupervised, semi-supervised and reinforcement learning) including tuning and regularizing these models.
  • Explain how these techniques work, including the relationship between more advanced methods and the simpler methods they are built upon.
  • Evaluate rigorously the performance of statistical models, and justify the selection of particular models for use.
  • Evaluate rigorously the sufficiency of and suitability of data for a given modelling task

Requirements

Sections of the course make use of advanced mathematics, including statistics, linear algebra, calculus and information theory. If you have prior knowledge of these areas, particularly the first two, you will obtain additional insights into the methods used. If you do not have this prior knowledge, you will still be able to achieve the learning outcomes of the course.

Instructors

Michael Ashcroft

Mike Ashcroft is Chief AI Officer with Persontyle, researches and teaches at Uppsala University, Sweden, and has founded two companies specializing in AI/ML consultancy and project management.

How To Join This Course

  • Go to the course website link
  • Sign Up At FutureLearn
  • Select a course and Join
  • Once a course has started, applicant will be able to access the course material
  • After the start date, students will be able to access the course by following the Go To Course link on My Courses page.
  • Applicants can buy, to show that they have completed a FutureLearn course.
  • On some FutureLearn courses, learners will be able to pay to take an exam to qualify for a Statement of Attainment. (These are university-branded, printed certificates that provide proof of learning on the course topic(s)).

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