Machine Learning A-Zâ„¢

It made me understand how machine learning really works..

Definitely extremely well designed and easy to digest course and anyone aspiring to learn Machine Learning and Artificial Intelligence must go through this beautiful course.

They taught the basics of Machine Learning absolutely well, and ML is much less scarier coming out of the course.

Those are really helpful to understand basics of the Machine Learning and preparing you to starting new journey about Deep Learning.

It will provide all the required basics to get you started on Machine learning.

It scratches the surface of machine learning and I think it is a good start for someone who wants to enter this field.

You will learn to implement the Machine Learning tools in Python and also R. Then, in gerenal, it's a good course about introduction to Machine Learning.

Firstly, the theory behind the Machine Learning algorithms sometimes is too superficial.

TLDR course needs some updating but the first parts seem great Very straightforward and interesting course to learn about machine learning and deep learning from scratch.

It is the perfect course to get started on the Machine Learning journey.

This is a good course for every new rookie enthusiastic about the machine learning area and its application in the business world.

Here is my review: Good things: - This course covers nearly every important topic in machine learning.

Thanks to Kirill Eremenko and Hadelin de Ponteves for wonderful course design.

Most importantly it teaches you to choose the right model for each type of problem.Thanks Kirill Eremenko and Hadelin de Ponteves for this opportunity ..!

And now very excited to pick my new course "Data Science A-Z & Deep Learning" Thanks alot kirill eremenko, Hadelin de Ponteves and SuperDataScience team for such a wonderful course.

Very nice Thanks for Kirill Eremenko Hadelin de Ponteves.

This is an amazing course put together by Kirill Eremenko and Hadelin de Ponteves.

Overall a very good learning experience and my very first one online :) Kirill Eremenko and Hadelin de Ponteves are a really good team.

Kirill Eremenko and Hadelin de Ponteves do a great job with the way the course is structured and communicated.

especially i would like to thanks for Kirill Eremenko, Hadelin de Ponteves and entire super data science team for there effort.

He joins forces with Hadelin de Ponteves and together they create another first-rate course.

Thanks you kirill eremenko and hadelin de ponteves I give 4 stars because while the course is amazing, it does not include future prediction.

Thank you Kirill Eremenko and Hadelin de Ponteves.

Thanks Kirill Eremeko and Hadelin de Ponteves.

Amazing course.... Having the detailed 'intution' behind the complex Data Science , Machine Learning and Neural Network concepts helped in quickly grasping the topics.

This is a very useful course to learn how to implement difference machine learning techniques (regression, classification, clustering, deep learning, convolutional neural network, dimensional reduction in python and R. The main shortcoming of this course is that it does not have homework or project to make you exercise.

The only small issue I had with the whole course was in the Neural Network parts where I didn't understand some aspects but I firmly believe I shall revisit that aspect of this course to redo it so as to get a good understanding of it.

XGBoosting is covered in the class which is cutting edge and not found elsewhere on Udemy; however, I wish they added a video on the intuition to explain why it can beat neural networks.

The main instructor rambled way too much, especially in the artificial and convoluted neural networks part.

Amazing learning from this course - i just expected that the course could discuss the concepts behind the Neural networking for a dummy.

would love to get more in-depth in the neural network section.

Excellent delivery of concepts I enrolled in this course just for the Neural Network topics and Kirill is extremely good at explaining the concepts.

I only hope they create more advanced and specialized courses targeting Recurrent Neural Networks and Boltzman Machines.

Basic idea of what a neural network is and how it is works is demonstrated really well.

Like the simplicity and reinforced learning It was a great learning experience about Machine Learning for beginners especially the topics like Regression and Neural networks.

After that I'll recommend to go through Prof. Andrew Ng's Machine Learning course on Coursera or YouTube.

I just had completed the ML course by Andrew NG on coursera, so I had a base good enough to get along with the implementation of the algorithms in Python (I am avoiding R for now).

I really like that it includes information for both Python and R users This is a wonderful course, came to this course after completing Andrew Ng's course on Coursera.

After absorbing the theoretical-cum-intuitive style of teaching of Andrew Ng, I needed some hands-on.

Tip: Do this course along with coursera's ML course (Andrew NG's) to get the maximum output.

Great course on Applied Machine Learning, I think this course should be taken after Andrew Ng Machine Learning course so you know the theories of ML algorithms and then how to apply them.

The instructor is awesome who explains each algorithm in simplest possible way without mathematical complications (even though I was looking for it for which I referred Andrew NG course).

I found the course from Andrew NG very deep in theory while this course is moderately deep in theory and also has many example exercises used to explain various algorithms.

For me, too much of theory makes it hard to stay attentive, which is why I liked this course better than the one from Andrew NG, though the course from Andrew NG is a must take and has a lot of theoretical depth necessary for ML.

However, if you are like me who prefers watching things in action more than theory, this is the course you must take first, followed by the one from Andrew NG.

A much better and free course that will help you understand machine learning will be that from Andrew Ng.

Not too much mathematics explained though I would have liked that - but I also took Andrew Ng's course, so that helped my mathematics appetite while this provided for a more applied platform.

Fully Satisfied The course covered a lot of fascinating topics, my favorite ones were the Convolutional Neural Networks, Multi-Armed Bandit problem and Grid Search algorithm.

In one lecture on Artificial Neural Networks the take away was 'if you are interested in the topic read it yourself, here are the papers'.

It became much more interesting and challenging as I progressed to neural networks.

*5/5* I particularly enjoyed the practical emphasis, as I learned to import and manipulate data, apply regression and clustering techniques, and create Neural Networks in a logical step-by-step approach.

Not covered was saving the state of your Neural Networks.

I really enjoyed the course, particularly the Convolution Neural Networks for computer vision.

During the neural networks and XGBoost chapters, installing required packages got very complex and instruction for it were very difficult to follow especially since he was on mac and was not fully step by step.

You end up with a good idea of classification, regression and artificial neural networks (and some NLP), with working examples in all cases.

Convolutional neural networks is such a wide field ---certainly couls have explained it a little better Good comprehensive course for learning the basics.

There was also a nice little introduction to Deep Learning and Neural Networks.

Update: Was not able to get the libraries required to run section 31: Artificial Neural Networks so I was unable to finish this course.

I'm a computer scientist with a Master in Computer Sciences from ETH Zürich that I got in 1995 where I already took classes in AI especially in artificial neural networks.

Only the support vector regression was kind of weird.

much intresting and i got what i needed All really clear to understand, then you get to the support vector regression bit and it is very confusing.

But in some cases even the intuition part is missing like for SVR (Support Vector Regression) or NLP (Natural Language Processing) where the instructor jumps directly to coding.

though I have completed the course, my journey in this field just began Support vector regression intuition needs to be improved.

Apart from the support vector regression part I thoroughly enjoyed this course .

I had to reduce the rating when I went through the Support Vector Regression intuition.

The only lesson I found hard to follow was on Support Vector Regression -- it seemed to assume that you knew things that I certainly didn't know.

Very complete statistics models course with Python and R demonstration This course is definitely a match for me and since i am doing my Masters in Radiation science it will definitely come in handy Great course - so I am so excited that I want to try another courses by these authors))) This course is good but there are somethings that need to be improved such as *There should be sufficient exercises because same data doesn't gives experience to the learner(As this course is for job purposes) *There should be some serious improvisation in in "SUPPORT VECTOR REGRESSION" .I am not able to understand a single thing and then i saw the Q&A everyone is complaining about that section .

It would have helped if new topics like Support Vector Regression were introduced before implementation in Python / R. Excellent Course indeed Organized.

I think the only difficulty was on Support Vector Regression, since the explanation of the intuition section was not really very intuitive or clear.

i think its good Covering wide range of topics.

Overall, there was a lot of useful content in this course covering a wide range of topics and I was satisfied with the amount of things that I learned.

The wide range of topics helped me put several topics in the right perspective, and how to choose an algorithm.

This course contains wide range of topics related to ML.

Covers a wide range of topics.

Practical way of applying Machine Learning using Python and R Slightly ponderous tool introductions, otherwise fine Wide range of topics covered very well.

Very wide range of topics and passionately taught The course is no doubt is great.

Covers a wide range of topics on machine learning.

Step by step instructions to build the templates and then re-using the templates in the class builds familiarity which will make it easy for someone to come back and refer to these examples in the future when working on their own ML problems.

Clear step by step instructions.

great step by step instructions The course is ok so far.

Lucid explanations and clear step by step instructions in both R and Python.

It's a good start, Great course the course explain fundamental of each machine learning technique and hands-on experience in Python and R. this make the course enriching yet not boring very clear and step by step instructions Needs a little more theory about how mathematical models of mathematical regression works.

It's really inspiring and gives you hands-on step by step instructions.

Step by Step instructions seem to be very clear and easy to understand.

Suggestions for the instructors: -Add some more intuition sections, particularly for SVR and for XGBoost -The section on natural language processing doesn't seem to fit with the rest of the course.

Audio and video quality are poor in many sessions like reinforcement learning, natural language processing etc.

I particularly enjoyed the section where we compared different classification models for their performance in Natural language processing.

I would have liked additional real world examples, especially on Natural language processing.

So far, I have gone through only the Natural Language Processing for solving my assignment.

The instructor walked me through the basics of the natural language processing and the best part that I like the most is the way he explained the whole working of the procedure, giving a good idea of how the whole process is going to work from starting till the end which helped me get a clear idea to apply this knowledge to other datasets on my own.

The sound is bad and there're some lessons that the closed caption doesn't work It is a great experience Not beginner friendly the course prerequisites say only high school math well then why the hell did the instructor just rush through all the libraries and the math plus the course is not offering me a refund it is just 6 days since i bought the course and i have watched only 46 videos this is not done Udemy this is not Done you must protect the students interests also Best course in the market.

Everyting is clear and informative, but I think the introduction to the course is misleading, it is more complicated than just some high school math.

The requirement is high school math.

Keep it up Great course so far - One star removed because the course claims you only need high school math to dive right in but that's not really the case - They will quickly provide you with all the R and Python code you need to get their specific example model up and running, but they really don't explain the code syntax or alternatives or other helpful tips so it seems to me that taking a in depth R and Python class prior to this would be extremely beneficial.

The course prerequisite says high school math, but the looks like we also need programming knowledge.

It gets into the details of writing the code, but is still pitched at a level that those with a non-computer science and high school maths can get up to speed.

This course is great I would say that only needing some high school math isn't really fair though having programming experience is helpful.

Some parts such as "Multiple Linear Regression" and a few from the 'Deep Learning' section were quite challenging to understand.

Until multiple linear regression lectures it is really intuitive and descriptive.

I had a lot of things to learn but after the multiple linear regression I felt this course is less engaging.

Other crucial aspects of statistical modeling were omitted entirely: Consider the exercises for multiple linear regression with categorical variables using backward elimination.

Clarté des explications I reached till Multiple Linear Regression.

You are giving directions to load the wrong files for the courses segments.Multiple Linear regression segment tells us to load the k-nearest neighbor files.

Multiple linear regression (Sec.

basics of R; or multiple linear regression).

Seems like great content so far basic introduction - defitnition of machine learning, how it is connected to artificial intelligence , what exactly is learning what is intelligence I want to further my career into ML technologies and from what I've seen so far, I believe this course is going to deliver that for me.

I would recommend Machine Learning to anyone interested in modern data science who won’t be terrified by Greek letters mixed with numbers and wants to peek behind the “artificial intelligence” curtain to find the insomniac data scientists trying to determine if a hidden layer of 8 neurons will produce more accurate results than 9.

This course has opened my mind on how to use Artificial Intelligence in a strategical way to produce more value for nature, the human kind and the stakeholders.

Looking forward to Artificial Intelligence A-Z and Deep Learning A-Z also.

Covers almost all aspects related to machine learning and artificial intelligence.

would recommend to anyone who loves mathematics and takes interest in artificial intelligence.

The best start for Artificial Intelligence!

Plus, the content is interesting if Artificial Intelligence or Robotics is of any interest.

I am extracting the same information as I would get from reading a basic book on Python and Artificial Intelligence/machine learning.