Machine Learning (ML) · Beginner level

Beginner level · Module 1 of 5 · 10 guided hours

Machine Learning Foundations

Understand what machine learning is, the kinds of problems it solves, the steps of an ML project and the tools used in this program.

Learning objectives

  • Explain supervised and unsupervised learning with local examples
  • Describe the steps of a machine learning project
  • Set up Python, Jupyter or Colab, pandas and scikit-learn

Topics

  • rules vs learning from data
  • supervised, unsupervised and reinforcement learning
  • regression vs classification
  • the ML workflow
  • Jupyter notebooks and Google Colab
  • pandas and scikit-learn basics

Lessons

  1. Lesson 1 What Machine Learning Is

    See how learning from examples differs from writing rules, and meet the main types of machine learning.

    modelfeatures and labelssupervised vs unsupervisedregression vs classification

  2. Lesson 2 The Machine Learning Workflow

    Follow the steps of a real ML project from question to monitored model, and see where most of the effort goes.

    problem framingdata collection and preparationtraining and evaluationdeployment and monitoring

  3. Lesson 3 Notebooks, pandas and scikit-learn

    Set up the tools, load a data set with pandas and train a first model with scikit-learn's fit and predict.

    notebookDataFrameestimatorfit and predict

Practical activity

Classify twelve real-world problems (from crop yields to fraud detection) as regression, classification, clustering or 'not an ML problem', and justify each answer.

Expected outcomes

  • A clear mental model of what ML can and cannot do
  • A working notebook environment

Assessment

Short quiz on ML types and the workflow, plus a check that each learner's environment loads a data set and trains a demonstration model.

Free taster lesson What is Machine Learning? & Types of Learning Read a free self-study lesson on this topic in the Free Course Library.

Taught live by an instructor — online or in person. Free course lessons are for self-study and do not lead to a certificate — see registration options.