In the second course of the Machine Learning Specialization, you will: • Build and train a neural network with TensorFlow to perform multi-class classification • Apply best practices for machine learning development so that your models generalize to data and tasks in the real world • Build and use decision trees and tree ensemble methods, including random forests and boosted trees The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn 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 and expanded 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 theoretical 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.
MP
One of the best course for Learning Neural Network Fundamentals also dive into the advance algorithm like Decision trees,Random forest or XGB Algorithm. Thanks Andrew Ng for the top-notch course.
AA
The course provides an excellent introduction to widely used machine learning concepts, including Neural Networks. While the material can be challenging, it is presented in a digestible manner.
MN
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.
AH
Beginner level course, explained simple concepts on neural network specifically Multi-Layer Perceptron & Decision Tree, nothing advanced topics covered. But the explanation is very very good.
SD
Great course! and according to me, the ML roadmap that best matches the one I thought to approach the ML topic based on all my experiences. So I recommend this course of Andrew to everyone.
AK
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🤯
AV
Amazing content, perfectly curated topics with hands-on labs, although Assignments and labs could be more challenging based on certain level students who already have programming backgrounds.
AM
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.
LL
Worth it! Can easily be complete within a month (even during a full, in-person, university class load). I used this to learn more about machine learning prior to conducting biomedical research.
HS
This course is Amazing, but the only thing is that the background is too much bright!! While most of the people (including me) learns at night, and that bright screen just ruins the focus.
MW
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!
CG
Good exposure to ML concepts but the labs were a little too easy. I think if they ever redesigned the course they should give the student the option of coding the algorithms from scratch.
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Slightly disappointed with the assignments to be honest, most of them are too easy to solve, and moreover can be just copypasted from the hints.
Great theory which lacks some demanding practice tasks.
Seems lacking some mathematical details like how to calculate Backpropagation this time
Excellent course, although it would have been good to talk more about backward propagation, after finishing this course this is the only point that is left unclear in my mind.
The content is excellent but some more emphasis must be given on the discussion of the codes in the Jupyter Notebooks otherwise it'll become less appealing to the once who don't have a good grasp over Python.
I was really hoping more detailes on xgboost and unfortunately the course level was very elementary
I finished this course with 3 weeks left in my monthly subscription, only to find out the 3rd course wouldn't be available until the day my subscription ended. I essentially paid for a specialization that didn't exist at the time of my purchase.
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.
Just as in the other courses in the specialization, this provides solid conceptual overview, but: 1) too little conceptual practice (the quizzes are short and inanely easy) 2) too confusing labs (for someone with plenty of JS, particularly web-related, but no prior python/data-science experience, I struggled to understand how the data structures were being manipulated and to intuit what the helper functions were asking me to do, and plenty of numpy/python errors that I had to interrogate for too long, distracting from the core concept. On the latter point, I laud their attempt to scaffold with hints, but this needs revision).
Great course! and according to me, the ML roadmap that best matches the one I thought to approach the ML topic based on all my experiences. So I recommend this course of Andrew to everyone.
Good introductionary course to advanced learning algorithms.
Main point of feedback: the course did not address in anyway how to actually desigining neural networks, i.e choosing layer size, number of layers, etc. I thought this was a bit odd, since this is a rather fundamental part of NNs. Now that I've finished the course, I'm still not able to build my own NNs from scratch, since I don't know how to choose my layers. I hope further courses will address this, but I think this should've been addressed in this course already.
Nicely presented, however the assignement and the material was too basic.
I would have liked a more rigurous introduction to the subject, that being said I feel I did get something out of this course.
There are way too many errors on the video's subtitles - not grammatical mistakes, but words completely out of context or even incorrect formulas.
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.
Enjoyed thoroughly the course. The mathematics concepts we’re well explained through exercises that help us visualize the concept behind each equation. The exercises were well thought out to help the student bridge theory to practical coding. People not familiar with mathematical coding will start to understand the pattern behind and be self-sufficient. Thank you for building this wonderful course.
This was one of the best courses I have ever experienced. There was a subtle beauty in the course's planning and Dr Andrew's teaching. The effort Andrew sir made in his teaching was quite evident, and there was a remarkable balance in the difficulty of the course - no matter whether you are a beginner or experienced with Machine learning, you will enjoy this course!
A very beginner friendly course that has great explaination on the topic. The programming assignments are really simple and I hope to see harder assignments in course 3 which would allow us to put everything we learn into practice
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.
The course was fantastic! I really enjoyed every part, every video, every quiz, every optional lab, every assignment of the course. It was a pretty memorable ride to have come this far.
The course expands the subject of machine learning in an appropriate and interesting way. Unfortunately, the quality of the downloadable material is insufficient, as the course describes multiple graphs and equations with numerous superscripts and subscripts, but unfortunately it is not possible to download the material in the slides, so as the complexity increases it becomes very difficult and stressful to take notes. This results in passive listening to the video, which certainly has a negative effect on real learning. Finally, each exercise contains the solution and it is therefore possible to pass the course passively without having learnt anything.
One of the best explanations of the backend of most advanced ML algorithms. The instructor is the best of the best. Learning new and Advanced Algorithms surely helps you to understand the newer technology and help your hunt down a good job in your future. Thanks to Coursera, Stanford, Deep Learning.AI and Andrew Ng for this very special course and for all of your hard work in making this course go smooth and easy to understand.