Supervised Machine Learning: Regression and Classification: Free Course

About this course

Study Supervised Machine Learning: Regression and Classification through this free English video course from DeepLearning.AI. The classroom brings together all 41 public recordings from the selected source curriculum, totaling approximately 5h 11m. You can watch the teaching sequence, search the lesson list and keep track of the recordings you have completed. The course focuses on the subject named in its title. Its value comes from following the explanations and making your own small practice attempts, with enough time to inspect the result and explain the decisions involved.

Course at a glance

What you’ll learn

• Explain supervised learning using input and target examples
• Describe linear regression and its cost function
• Trace how gradient descent changes model parameters
• Compare classification with numerical prediction
• Interpret logistic regression and classification boundaries
• Recognize overfitting and the purpose of regularization

Skills you’ll gain

• Linear regression
• Logistic regression
• Gradient descent
• Regularization

Course Overview

Course 1, Weeks 1–3 of the Machine Learning Specialization; other specialization courses are not in this source.

The source lesson list includes “#1 Machine Learning Specialization [Course 1, Week 1, Lesson 1]”, “#21 Machine Learning Specialization [Course 1, Week 2, Lesson 1]”, “#41 Machine Learning Specialization [Course 1, Week 3, Lesson 4]”. The classroom retains their original titles and positions. Introductions, supplementary explanations and complete-course recordings remain present whenever they are part of the source. A repeated recording is retained at each original position, allowing the course to be checked against the published teaching sequence.

Supervised learning and regression

Establish what the model learns from labelled examples. Connect the representation of the data with a prediction rule, and explain how a cost function measures disagreement between predictions and targets.

Optimization and implementation

Trace the gradient-descent updates shown in the teaching sequence. Check the meaning of the learning rate and the roles of parameters, then follow the vectorized implementation ideas used to make calculations systematic.

Classification

Move from continuous numerical predictions to classification. Explain why logistic regression uses a different prediction form, and relate probabilities, thresholds and decision boundaries to the training examples.

Generalization

Connect the regularization explanations with the problem of overfitting. Compare training behaviour with performance on new examples and explain why a more elaborate fitted model can be a weaker practical choice.

Who is this for?

This course suits learners who want the specific subject described above and who can work at the stated level. It is also useful as a recorded reference when revisiting a concept or comparing a practice attempt with a demonstrated example. Beginners should give the foundational explanations time before moving to more elaborate applications. Experienced learners can use the lesson names to locate a topic, then inspect the surrounding context before applying a technique.

Before you start

Basic Python familiarity and secondary-school algebra are useful. The recorded explanations introduce additional mathematics as needed. A Python notebook environment is helpful for optional independent practice.

Put the lessons into practice

Create a small fictional dataset with one numeric input and one target. Fit a simple baseline, record prediction errors and explain how a parameter change affects the result. Then describe a related binary-classification problem and choose a sensible interpretation of its predictions. Compare a training result with a held-out check. Keep the example small enough that you can explain the calculation without treating a library call as the entire solution.

Treat this suggested exercise as independent practice provided by WikiFree. Keep a short working note containing your inputs, assumptions, result and one point you still need to check. Revisit the relevant recording when a step is unclear, then attempt a small variation without following the demonstration line by line. A result you can explain and reproduce is a more useful study outcome than simply recognizing a familiar term. The exercise is not submitted to an instructor for grading.

Certificate / Assessment

Watching these public recordings and using WikiFree completion tracking does not award a certificate, academic credit, an examination voucher or instructor assessment. The video curriculum is the material included here. Separate platform assignments, credentials and paid services are not part of video viewing. Use the practice output to review which concepts you can explain, which steps you can repeat independently and which questions need another look at the teaching.

Frequently asked questions

Provider: DeepLearning.AI. Videos play from the DeepLearning.AI channel on YouTube.