The quizzes and labs are too easy to be of value, with many of the quiz answers literally being written in the image displayed above the question, and labs basically just require you to translate a specific equation into Python.

There isn't really a way to tell if I understand the content or not, I recommend you do not pay for this course.

more emphasis on coding, notations etc.

The 'Advanced Learning Algorithms' course, part of Andrew Ng's Machine Learning specialization, has been an invaluable resource for anyone looking to deepen their knowledge of advanced machine learning techniques. The course content, which delves into Neural Networks, model implementation in Python, AGI, Vectorization, Training, Activation Function, Classification, Back propagation, Bias and Variance, Decision Trees, and tree ensembles, has provided a comprehensive understanding of these complex algorithms. The practical implementation and real-world examples, along with the contributions of Eddy Shyu, Curriculum Architect, Aarti Bagul, Curriculum Engineer, and Geoff Ladwig, Curriculum Engineer, have made the learning experience both informative and engaging. Their expertise and dedication to the course content, combined with Andrew Ng's clear teaching style, have truly made this course a standout in the field of machine learning education. I highly recommend this course to anyone looking to advance their skills in machine learning algorithms."

Amazing courses that go into detailed explanations about the math and intuitions behind the algorithms without getting too convoluted or making things unnecessarily complicated just for the sake of it.

Prof. Andrew doesn’t just tell you the name of a function for a library (like scikit

learn or tensorflow) and give you magic numbers for parameters. You actually build the model yourself and learn what the parameters stand for and what is the purpose of those parameters and hyper-parameters.

The specialization is well divided into meaningful courses and each course is well structured so that you know exactly what you are going to learn and what key specific skills you will get after completion of a course. Because of the quizzes and practical labs, after completing a course you actually gain confidence that you can design optimized solutions for that particular set of problems.

Again like the previous course , the way of teaching has been exceptional . It motivates me to go look for data sets on all the types of projects that I am enabled to imagin only by the efforts of Sir Andrew and build projects on them . Also I think that completing the course won't land you a job . You really need to put you work and effort on projects and become really good with the skills that is being taught . Making notes is a must because there are a lots of topics being covered in very short span of time. Binge watching the lectures is not good if you really want to learn AI ML . Those topics might wipe off your brain in no time unless you are some super genius being from another planet . Also don't forget to download the labfiles for refrence . They are great source for revison . Thanks

I recently completed the 'Advanced Learning Algorithms' course on Coursera, and it was an incredible learning experience. The course dives deep into advanced machine learning techniques, including reinforcement learning and deep learning models, providing a comprehensive understanding of how these algorithms can be applied to real-world problems. The balance between theoretical concepts and practical exercises made it easy to grasp even the most complex ideas. The hands-on projects were particularly helpful in cementing my knowledge and allowed me to apply what I learned. The course is ideal for anyone with a solid foundation in machine learning who wants to advance their skills. Overall, I highly recommend it for anyone looking to expand their expertise in advanced learning algorithms.

I really appreciate the effort put in by many people, including Andrew Ng and I gave it a 5/5 for so many things, such as intuitive teaching and making the classes and labs enjoyable and fun. But, unfortunately, I couldn't find one thing just not enough and it was, for students like me, who want to enjoy the subject in depth, the course remained a little bit less mathematical, and hence the question why? remained unanswered. I understand that the course is beginner-friendly. But I am a beginner too, and I want to learn more. Though I could do that from other sources, It is always enjoyable when it comes from you. Again, Thank you very much for your time and effort in making Education online, very much helps people across the world.

A great overview of neural networks, decision trees/tree ensembles, and most importantly (in my opinion) guidelines for implementing these algorithms well - assessing for overfitting, ascertaining what next steps to take to improve model performance, etc. I would have liked to see a bit more coverage - for example, in optional videos/labs - on the details of random forests and XGBoost. The coverage of decision trees overall felt more sparse and a bit rushed compared to the coverage of neural networks, and left me with some unanswered questions about how to appropriately implement these algorithms. As with the first course of the specialization, the lecture videos were always clear and engaging - truly excellent.

Dr. Andrew Ng and his Team have created a solid ML course that is both beginners friendly and highly intuitive. The theory,just like the first course in the specialization, has been explained in a very lucid and friendly manner. They have created a course that is really good at imparting all the necessary theory to learners without bagging them down with all the complicated maths related to them, which is something I have found a very good thing. Plus , they also touch upon all the open source ML libraries that is used by the industry and enthusiasts alike, which is something I really appreciated.

All in all, this is one of the best ML course out their and a must learn for every beginner ML enthusiast.

I've been working day and night on my career-shift to be a ML engineer for the past few months and I have to say that out of all the courses I've taken to this point - and that is a lot - this one is the best hands down. The course delves deeper into the concepts, guides you to implement algorithms yourself and supports your knowledge with fun quizzes, awesome labs and optional videos. The tone and attitude of Andrew makes you always looking forward to the next video, and there was nothing more sad than hearing the words "this would be the last video of this course", luckily there are still more courses offered by the deeplearning.ai team.

i love the way Andrew Ng teaches, i remebered a while back, i tried learning about decision tree from the internet and i could remember , i left the tutorial more confuse. i love the way he presents the learning intuition of each topic. for everyone watching & taking the course, watch out for the "intuition" of each topic. Andrew Ng does a great job of demystifying ml concepts ( than most youtubers that dive aimlessly into jupyter notebook using libraies without explaining the concepts) and also partake in the quiz and assesment, they are great help.

it was a nice experience learning, Thanks Andrew Ng, DeepLearningAi, Stanfordonline

Andrew Ng's Advanced Learning Algorithms course has given my understanding of complicated machine learning ideas a real "XGBoost." It's also taught me about neural networks and decision trees. The course is organized well, mixing clear explanations with hands-on practice in assignments. This has made me more sure of understanding tricky algorithms and how they're used, and how they connect to neural networks and decision trees. Professor Ng's teaching is very clear. This top-notch 5-star course is a must if you want to get better at these important parts of machine learning and make a big difference in the field.

Great course. All the introductory subject matter that one may need to start learning about advanced algorithms, plus coding support labs to facilitate learning. One added feature could be giving more room to the learners to practice coding. I know this is not a coding course but an optional practice lab that encourages the learners to code more independently would provide the necessary practice to fixate one's knowledge.

In addition, I wish the course had also focused on the process of data gathering, or using available data on the net to guide the learners towards independent practice of ML principles.

The curriculum is well-structured, providing a mix of theoretical knowledge and practical application through programming assignments. This allows students to implement various algorithms and understand their strengths and limitations in different scenarios. Additionally, the course emphasizes the importance of model evaluation and the selection of appropriate metrics for different tasks. The instructor's explanations are clear and concise, making complex topics easier to grasp. However, some prior knowledge of basic machine learning concepts is recommended to fully benefit from the course

One of the best online courses available is a course focused on mathematical concepts. This course stands out because it offers highly valuable practice classes in addition to comprehensive content. The course is designed to be accessible and easy to understand, using simple English to explain complex mathematical ideas. It provides a strong foundation in mathematical principles and offers practical applications to enhance learning. Overall, this course is highly recommended for individuals seeking to improve their mathematical skills in an online learning environment

Andrew Ng is an exceptional instructor who has revolutionized online education in machine learning. His ability to break down complex concepts into simple, intuitive explanations is truly remarkable. The structured approach, engaging content, and practical examples in his courses make them a gold standard for learners worldwide. His passion for teaching and dedication to democratizing AI education are truly inspiring. Thank you for making advanced knowledge so accessible and for empowering countless learners to step into the field of AI.

A great course that explains and walks you through actually coding principal supervised learning algorithms. Would appreciate if the description on calculating back propagation from the original course had been included. Then again, this version of the course I actually got through :). All in all I felt the assignments where at a level that I could quite quickly work through, and find help when I got stuck. Grateful that Andrew Ng and his team made this course series subject so accesible and doable.

Another typical Andrew Ng success! What else to say if not that the inspirational energy and always great sense of humour of Andrew make things silky smooth to learn. Information seems to be up to date with state of the art in general and the extra, optional information is always a blessing to have, gives the course that which I aimed to learn which is how do the engines of AI/ML work! All in a way easy to follow and to understand, I can't wait to be able to progress and put everything into practice!

I recently completed a course led by Sir Andrew Ng, focusing on Neural Networks and Decision Trees. The course's content is highly effective, though some parts proved challenging to comprehend fully. Sir Andrew Ng's instruction is commendable; his approach to teaching Neural Networks is simply fabulous. While the course's complexity occasionally posed difficulties, the overall learning experience was immensely valuable and has left me with a solid grasp of these crucial machine learning concepts.

Nice and informative course on the basics of advanced machine learning. Assisted with well programmed lab assignment, I got to learn a lot of machine algorithms from diagnostic ml, bias and variance and their importance for calculation of losses and finally decision tree equipped with several tools like random forest, XGboost etc...The information gained on completion of this course is surely going to help me come a long way in doing my phd project effectively with the help of machine learning

Andrew Ng is the best teacher in the world, and the way the course has been structured is right exactly what a beginner like me needs to have. What I really appreciated, besides the easy and rich contents (simple but not simplistic), is the many questions that Andrew answers along the way that somehow anticipates what comes to the student's mind - this gives the feeling that Andrew himself walked in the student's shoes before becoming one of the most prominent experts in the subject matter.

This is by far the best advanced machine learning algorithm course I've ever taken. Using simple examples to convey several no so intuitive concepts. Some of the concepts that was not 100% clear prior to this course are now crystal clear. But honestly, I would say for those who have zero experience in the field of machine learning and coding might find it hard to take in, especially the part when trying to write and understand the algorithm from scratch in the lab.

Great course. Andrew Ng teaches in a way that even a beginner with zero background knowledge about machine learning can understand pretty easily. The pacing is really good. The optional labs though mentioned as optional are extremely beneficial as the videos cover the theoretical part while the labs cover the basics of coding required. 100% recommended course if you are looking to learn machine learning as a beginner for academics or just to improve your skills.

This course is well designed for a beginner who wants to get the intuition of deep learning and see what is under the hood. By taking this course you will have a better understanding of principal concepts in deep learning and artificial intelligence and deep learning does not appear as a black box. The lab assignments are well designed to teach you better the whole concept. In a nutshell, well done Andrew NG, Stanford University, Deep learning AI, and Coursera!

Another excellent course. Dr. Ng's presentations are super clear and easy to follow, graphics/animations are superb.

What I think this course needs is a bit more time/explanation on backward propagation. Here it was touched upon only briefly and in an optional lab, and it was not clear how it tied back into neural networks algorithms. I had to learn this separately on my own by finding articles on Google. Thanks and looking forward to the next course!