Duration of the online course: 25 hours and 9 minutes
4.78
(116)
Machine learning is the skill that turns raw data into decisions: predicting demand, spotting fraud, ranking search results, recommending products, or grouping customers by behavior. This free online course guides you from core principles to practical ways of thinking that modern teams use when building intelligent systems with Python. Instead of treating algorithms as black boxes, you will learn how to reason about model choice, optimization, and generalization so your solutions work beyond a single dataset.
You will develop an intuition for how learning algorithms improve with experience and what it means to balance bias and variance when your model meets real-world noise. Along the way, you explore foundational supervised methods for regression and classification, then progress to probabilistic approaches often used in text and event modeling. You will also connect the dots between decision boundaries and margins, understanding why support vector machines can be powerful and how kernels help models capture non-linear structure without exploding complexity.
A key focus is learning to make models reliable. You will see how expanding the hypothesis space can affect generalization, how complexity measures shape what we can guarantee, and how to think about performance when data is limited. You will also practice unsupervised learning to uncover structure in unlabeled datasets, including clustering to find groups and dimensionality reduction to simplify features while keeping the signal. This mindset is invaluable for cleaning datasets, compressing information, and preparing inputs for downstream models.
The course goes further by introducing sequential decision-making ideas that underpin reinforcement learning. You will learn how Markov decision processes frame control and planning, why partial observability makes policy computation difficult, and how dynamic programming style methods approximate good decisions in large spaces. These topics build a strong conceptual base for applications in robotics, operations, and automated decision systems.
With exercises that reinforce each concept, you will leave with a clearer understanding of the theory behind machine learning and the confidence to apply it in practical programming work. If you are aiming for a career shift, stronger data science fundamentals, or better AI fluency for software projects, this course offers a rigorous path to leveling up while learning at your own pace.
Video class: Lecture 1 | Machine Learning (Stanford)
1h08m
Exercise: What is emphasized as a key advantage of learning algorithms in the class?
Video class: Lecture 2 | Machine Learning (Stanford)
1h16m
Exercise: Which of the following describes what an “alvin” is?
Video class: Lecture 3 | Machine Learning (Stanford)
1h13m
Exercise: What is a key characteristic of locally weighted regression?
Video class: Lecture 4 | Machine Learning (Stanford)
1h13m
Exercise: What is one advantage of Newton's method over gradient descent?
Video class: Lecture 5 | Machine Learning (Stanford)
1h15m
Exercise: What do you learn in a discrimitive learning algorithms?
Video class: Lecture 6 | Machine Learning (Stanford)
1h13m
Exercise: How does Naive Bayes handle text classification differently in its event models?
Video class: Lecture 7 | Machine Learning (Stanford)
1h15m
Exercise: What is a key mathematical constraint in deriving the optimal margin classifier in support vector machines?
Video class: Lecture 8 | Machine Learning (Stanford)
1h17m
Exercise: Which algorithm allows SVMs to handle non-linearly separable data?
Video class: Lecture 9 | Machine Learning (Stanford)
1h14m
Exercise: What is the effect of expanding the hypothesis class on the generalization error?
Video class: Lecture 10 | Machine Learning (Stanford)
1h12m
Exercise: What is the VC dimension in the context of linear classifiers?
Video class: Lecture 11 | Machine Learning (Stanford)
1h22m
Exercise: His advices are good if you want to:
Video class: Lecture 12 | Machine Learning (Stanford)
1h14m
Exercise: What is the primary function of the K-means clustering algorithm discussed in the presentation?
Video class: Lecture 13 | Machine Learning (Stanford)
1h14m
Exercise: What key advantage does the EM algorithm provide when applied to complex models?
Video class: Lecture 14 | Machine Learning (Stanford)
1h20m
Exercise: What is a common application of PCA in data analysis?
Video class: Lecture 15 | Machine Learning (Stanford)
1h17m
Exercise: What is the primary goal of PCA in data analysis?
Video class: Lecture 16 | Machine Learning (Stanford)
1h13m
Exercise: What MDP means?
Video class: Lecture 17 | Machine Learning (Stanford)
1h17m
Exercise: What is fitted value iteration used for?
Video class: Lecture 18 | Machine Learning (Stanford)
1h16m
Exercise: What is the role of the Riccati Equation in finite Horizon LQR problems?
Video class: Lecture 19 | Machine Learning (Stanford)
1h15m
Exercise: Which of the following is the first step if you want to design a controller to a helicopter?
Video class: Lecture 20 | Machine Learning (Stanford)
1h16m
Exercise: What is a key challenge in computing the optimal policy for partially observable MDPs (POMDPs)?
25 hours and 9 minutes of online video course
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Course comments: Machine Learning
Students found the free course helpful for learning and skill development, describing it as knowledgeable and easy to understand. Some asked about lecture notes and mentioned low video quality.
Rahul kumar Khare
hello I'm indian and I would like to inform that I am really happy after getting this platform and courses.
KESHAVA MURTHY. A
very good App for learning and skill development
Joseph Newton-Akpor
which site is the lecture note posted
McAugustus Sapanga
..you ok í
grahamhconquer
I'm still working through this course bear in mind I've taught myself coding in a variety of languages also I spent weeks building circuits 8bit comp
Uday Kumar S
Course is knowledgeable, but low quality video.
grahamhconquer
excellent course understood it easier than in the military thank you, grahamconquer81@gmail.com Any information about employment thanks
Biswarup Das
Nice course
Siddhartha Mondal
very goof