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Improve your understanding of machine learning. Explore advanced techniques and how to use them in your data science projects.

5,338 enrolled on this course

Advanced Machine Learning
  • Duration

    4 weeks
  • Weekly study

    4 hours

Discover and apply advanced statistical machine learning techniques

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.

What topics will you cover?

  • Statistical Machine Learning Theory
  • Analysis and Evaluation of Statistical Models
  • Analysis of Data
  • Supervised Learning - Artificial Neural Networks
  • Supervised Learning - Kernel Methods
  • Unsupervised Learning - Clustering
  • Unsupervised Learning - Topic Modeling
  • Feature Engineering
  • Missing Data
  • Basic Reinforcement Learning
  • Basic Semi-Supervised Learning

Learning on this course

You can take this self-guided course and learn at your own pace. On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

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

Who is the course for?

This is an advanced course and some experience with machine learning, data science or statistical modeling is expected. Links will be provided to basic resources about assumed knowledge.

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.

What software or tools do you need?

The course uses R. If you have not programmed with R before, you should consider taking a quick introductory course, such as Try R.

Who will you learn with?

I am a Senior Data Science consultant and Lecturer in Artificial Intelligence at Uppsala University in Sweden.

Who developed the course?

The Open University

The Open University (OU) is the largest academic institution in the UK and a world leader in flexible distance learning, with a mission to be open to people, places, methods and ideas.

  • Established

    1969
  • Location

    Milton Keynes, UK
  • World ranking

    Top 510Source: Times Higher Education World University Rankings 2020

Persontyle

Persontyle is global research, education, strategy and product development company. We create value, transform industries and improve lives using science, design, engineering, and intelligence.

Endorsers and supporters

endorsed by

European Data Science Academy

Learning on FutureLearn

Your learning, your rules

  • Courses are split into weeks, activities, and steps, but you can complete them as quickly or slowly as you like
  • Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
  • Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

  • Experience the power of social learning, and get inspired by an international network of learners
  • Share ideas with your peers and course educators on every step of the course
  • Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

  • As you work through the course, use notifications and the Progress page to guide your learning
  • Whenever you’re ready, mark each step as complete, you’re in control
  • Complete 90% of course steps and all of the assessments to earn your certificate

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