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Supervised Machine Learning: Regression and Classification


Take part in the Supervised Machine Learning: Regression and Classification to gain foundational knowledge of modern machine learning and develop skills and competencies from industry experts. This course provides participants a broad introduction into supervised learning, unsupervised learning, as well as best practices from the industry.

This is the first of three courses within the Machine Learning Specialization offered by Coursera, created in collaboration with DeepLearning.AI and Stanford Online and taught by Andrew Ng. This programme is designed for beginners to give them a basic understanding of machine learning and how these techniques can be used to build real-world Artificial Intelligence (AI) applications.

Course overview

The beginner course is estimated to take 33 hours to complete, and includes 9 assessments. It is split up into three modules:

  • Introduction to Machine Learning (7 hours)
  • Regression with multiple input variables (9 hours)
  • Classification (16 hours)

Learners will be equipped with the tools to:

  • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn
  • Build and train supervised machine learning models for prediction and binary classification tasks

 Participants will receive a shareable certificate after completion. 

Next steps

After completing the Supervised Machine Learning: Regression and Classification course, participants can take part in the next two courses of the Machine Learning Specialization:

Training Offer Details

Target audience
Digital skills for the labour force.
Digital skills for ICT professionals and other digital experts.
Digital skills for all
Digital technology / specialisation
Digital skill level
Geographic scope - Country
Industry - field of education and training
Generic programmes and qualifications not further defined
Target language
Geographical sphere
International initiative
Typology of training opportunties
Learning activity
e-learning coursework
Assessment type
Training duration
Is this course free
Is the certificate / credential free
Part time intensive
Credential offered
Self-paced course