The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
The course was excellent, and I gained valuable knowledge throughout. I am also grateful for the financial aid, which allowed me to complete the program successfully. Thank you for this opportunity.
Another fantastic course by Andrew Ng! He covers neural networks, decision trees, random forest, and XGBoost models really well. I like that he shares his intuition behind every concept he explains.
This course has offered invaluable insights and clarity in understanding machine learning concepts. It was a nice journey towards understanding practical application and complex concepts made easy.
great course. Nice explanation even if you are not familiar with certain mathematical concepts Like Gradient. I would recommend having some mathematical base to ease the understanding of the course.
Really well explained course, keeping the lessons lengths short and explained in easy language. I am amazed at how much I feel I have learned in the last few weeks since I started this course.
Andrew was a great teacher, explaining complicated topics in a simple and intuitive way. The programming assignments helped to put theory into practice. A great place to start learning a new field!
E​xcellent course. Intended as a refresher, and had a better understanding of feauture engineering, scaling, and logistic regression. Good hands on labs were very practical, engaging and rewarding.
what amazing course, I had never thought that i could understand these complex ml algorithm but this course not only made me understand them also taught me create these models from scratch🤯
lecture videos, practical labs, and last video (conversations with andrew), all are awsome. Thank you for giving me a solid intuition about unsupervised learning and reinforcement learning
V​ery Engaging course. The instructor explains stuff in a way such that a student can develop a sound intuition of the mathematics behind the algorithms in addition to the implementation side of it
This showcases key points and advice on building a good model via optimizing model hyperparameters hence making the learner able to debug and tune the model for the particular situation.
Very well on introduction the basic concept of the topics, but some of the function are not visible enough for us to understand (such as the update boolean in the assessment of reinforcement learning)
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I have completed this course in full and as a result, I am highly satisfued with how Professor Andrew Ng explains the materials. Thank you for this! However, I cannot understand, why after completing the course a part of studying materials are not accessible, even though I paid a sufficient price for the course. These unaccessible materials include Python programs which were used as a practice. Frankly, I find it unfair, since this practice would be extremely important to revise the materials while improving my skills in Machine Learning in the future. Moreover, a part of the montly fee was paid also for the practice materials. I may agree that these Python programs can be private, however,there should be ways to overcome this issue. Without the possibility to revise the code it will be much harder to create our own applications and programs.
One of the best courses out there on Machine Learning. Clean, Crisp and up to the point. Short but delivers all the things one need. More better than a classroom program. Saves one's time and energy.
tldr The course is a great introduction to ML for an audience already comfortable with mathematics and Python. For what it aims to achieve, I think it does a great job. /tldr
The mathematics involved in the first course of this specialisation is not that difficult if you already have a solid foundation on calculus. Some functions used in the Optional Labs are called for you from already written python scripts (which you have access to, and can download to inspect). The first 3 weeks (and probably the rest of the course) will not teach you fundamentals on Python or mathematics or statistics, and some details regarding the choice of loss function for logistic regression were omitted. Furthermore, libraries such as scikit-learn were used to complement the material, but not explained in depth. (Granted, this course is not about Python libraries.)
All in all this seems like a great introduction to ML for people already comfortable with mathematics and Python.
If you already have the foundations required (Undergrad basic calculus, Python) you can do all 3 weeks in one day fairly easily without distractions.
In general, I think it was a valuable course to take. I like the way Andrew tried to conveying the ideas intuitively to make sure the students understood the methods behind the learning algorithms. However, I would've loved if there was more in-depth treatment for the Math aspects of the obtained results. Also, the assignments + Optional labs were not as engaging as I hoped. What I mean by that is, it almost required no deep thought from our side to implement the procedures. In other words, there was a lot of skeleton code that makes you "implement" the algorithms with almost no thought (which I don't think is beneficial to the student's learning experience)
The course was extremely beginner friendly and easy to follow, loved the curriculum, learned a lot about various ML algorithms like linear, and logistic regression, and was a great overall experience.
The quizes are too straight forward and simple. The code exercise too short as well.
Also disappointed that vectorisation is introduced but cost and loss functions are still calculated in for loops.
It is the Best Course for Supervised Machine Learning!
Andrew Ng Sir has been like always has such important & difficult concepts of Supervised ML with such ease and great examples, Just amazing!
Amazingly delivered course! Very impressed. The concepts are communicated very clearly and concisely, making the course content very accessible to those without a maths or computer science background.
The course is good but once you cancel the subscription, you lose access to the codes. I think that should be change.
I feel like this course tries it's hardest, that everyone can follow it. But because of that it doesn't really dare to go deeper than just give an overview of machine learning. The tests are way too easy to pass with 100% and you can't really write your own algorithms afterwards. Also most of the examples are with one variable, so it's easier to follow, but it would be much more helpful, if we could see more complicated and real live examples.
Specacular course to learn the basics of ML. I was able to do it thanks to finnancial aid and I'm very grateful because this was really a great oportunity to learn. Looking forward to the next courses
Not a good course for beginners!!!! It should teach Python programming or have it as a prerequisite for the course. There should also be projects for the course
Excellent content. I'm a math guy so I would have enjoyed some more in-depth theory, but that's what books are for I suppose!
I've been using Python for a long time now so understanding the code was nice and easy.
Thank you for your hard work putting this together!
Professor Andrew can explain complex knowledge clearly. The Python lab can help learner to understand algorithm. The course is more valuable. I am excited to learn the next course for advanced ML.
I have just finished the old machine learning course, and I'm doing this because I'm learning python/numpy/matplotlib. I thought the question during the course and quizzes insulted my intelligence. The material is great, but you need to improve the simple questions and quizzes. The first programming assignment was too easy, the second programming assignment was at a fair level. I still think more should be left to the student to do.
You need to know Python, calculus and linear algebra for this course. I have a background in the last two, but never having used Python before, I'm unable to complete the final exam.
First too easy and at the last assignment no chance to get help for weeks. I can't finish this course. I don't see any difference from the hints towards my programming lines but still it's not working so I can not finish.
Teaching is an art and Andrew Ng is a great artist. He explained everything in the course in the details and with examples easy to comprehend. Thanks a lot for helping thousands of students like me.
if labs were optional then why are there compulsory coding assignments, labs must not be optional, instead make us type code step by step, like MATLAB onramp courses.