Free online courseMachine Learning
Duration of the online course: 25 hours and 9 minutes
4.77
(115)
Master Machine Learning with Stanford's free online course. Ideal for Information Technology enthusiasts, dive deep into 20 in-depth lectures on Python, Ruby, Java, and C.
In this free course, learn about
- Foundations of Machine Learning
- Logistic Regression and Generative vs Discriminative Learning
- Support Vector Machines
- Learning Theory and Generalization
- Unsupervised Learning and Mixture Models
- Dimensionality Reduction with PCA
- Markov Decision Processes and Value Methods
- Control and Reinforcement Learning Applications
Course Description
The "Machine Learning" course is a comprehensive and highly regarded program that spans a total duration of 25 hours and 9 minutes. This course has established itself as a standout educational resource within the Information Technology category and is specifically tailored towards individuals keen on acquiring specialized knowledge in multipurpose programming languages such as Python, Ruby, Java, and C.
Renowned for its in-depth content and expert instruction, the course has garnered an impressive average rating of 5 stars out of 5. Such high praise from previous participants underscores the course's effectiveness and the quality of the knowledge imparted. It has become a favored choice for aspiring machine learning practitioners and enthusiasts who seek to gain a solid foundation and practical insights into this cutting-edge field.
The curriculum is meticulously structured into a series of engaging and informative lectures, progressively covering key concepts and techniques essential for mastering machine learning. Starting with an introduction to the foundational principles, the course delves deeper, lecture by lecture, into advanced topics, ensuring learners build a cohesive and thorough understanding of the subject matter.
Throughout the course, students are guided through a rich tapestry of theoretical knowledge and practical applications. The lectures are designed to not only explain core concepts but also demonstrate their implementation using popular programming languages. As participants progress through the series, they gain hands-on experience and become proficient in applying machine learning algorithms and methodologies to real-world problems.
Overall, the "Machine Learning" course serves as an invaluable resource for anyone looking to delve into the realm of machine learning. Its meticulous approach, coupled with the high-quality delivery and practical exercises, ensures that learners are well-prepared to tackle the challenges and opportunities that come with mastering this transformative technology.
Course content
- 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)?
This free course includes:
25 hours and 9 minutes of online video course
Digital certificate of course completion (Free)
Exercises to train your knowledge
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Course comments: Machine Learning
Students found the free online course helpful for learning and skill development, praising it as excellent and easy to understand. Some noted low video quality and asked about lecture notes and employment information, but overall feedback was positive.
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