Advanced Machine Learning

Does anyone know if the advanced machine learning specialization (10 month program) from Coursera is good?

Well here is what I'm currently doing after having finished all the basic stuff in ML/DL + a MS in stats: https://www.coursera.org/specializations/aml and https://web.stanford.edu/class/cs224n/

There is also a course for computer vision.

These courses are graduate level but dont require a PHD. Now, finding a job that require this is harder (without PHD and outside tech giants) but from what I've seen it's because most analytic/data science department simply have no clue. I was able to work on an NLP project where I'm at because I brought it forward (it's not a complicated NLU problem mind you ;))

If you know Russian, then machine learning specialization by Yandex and MIPT on Coursera is really the best one. It covers a lot of topics: classification, regression, clustering, visualization, nlp, recommenders, statistics, time series and other things.

And now there is an advanced specialization by Yandex and Higher School of Economics - https://www.coursera.org/specializations/aml It is in English and quite difficult. It covers such topics as Deep Learning, Kaggle, Bayesian methods, Reinforcement learning, NLP and others.

I didn't say you were being disingenuous, I said it would be disingenuous to say that. The person in question said they wanted to pick up a new skill in ML, so based on that information I would assume they had no experience, and would benefit more from learning basic libraries and applications in of ML in Python. You responded to my comment that you felt like you were totally comfortable applying the skills you learned in a couple weeks of the course, and left out the part that you already had extensive experience in Python data analysis, so you are clearly not in the same boat as the OP.

At a high level, yeah jumping around languages is not a big deal. But this is a Python forum, Python is the language of ML nowadays, and I don't see the controversy in pointing out that a theoretical course in Octave is not a terribly good answer for someone looking for a Python course. And, by the way, there is also several deep theoretical courses in Python on Coursera with much better coursework then Andrew Ng's Machine Learning, I prefer this one (https://www.coursera.org/specializations/aml). Much longer course, but goes into much more depth, is more along the lines of combining Andrew Ng's Machine Learning course and his Deep Learning specialization, but with Python notebooks straight from the beginning. I don't think the Andrew Ng course is bad, it's just seriously overrated because it's been around a long time so even people who know almost nothing about ML know about the course.

I recommend Advanced Machine Learning specialization from Coursera. It covers stuff you mentioned (trees etc) and much more, like Bayesian methods (for example they cover Bayesian Optimization which is pretty useful in practice). The only problem is that specialization is all over the place, and some courses (CV course last I checked) have poor assignments - they're so underspecified that completing them is bordering impossible.

I know you mention that you've looked through Coursera but if you haven't seen this one or just flicked through it, Id recommend it.

https://www.coursera.org/specializations/aml

Maybe the whole course is a bit much for what you're looking for but they cut the course up into pieces. It's very Maths focussed. They have a section on NLP as well as general sections on Deep Learning and Computer Vision.

It's a few years old now so may not have all the very latest updates and techniques in the field but it's a very good 'deep' introduction that focusses very much on the maths side of things.

If you're an English speaker, some of the Russian accents can be tough to follow.

Advanced Machine Learning Specialisations: NRU vs. Google?

I am interested in taking one of the Advanced Machine Learning specialisations on Coursera.

I have previously done Andrew Ng's ML and DL courses, Koller's PGM one and I use ML occasionally at work so I have some experience and would like to go beyond the basics.

I am trying to choose between these two:

• Advanced Machine Learning Specialization by National Research University Higher School of Economics

• Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization

The Google course has the benefit that it is using GCP which we use at work but I'm concerned it might be more of a sales pitch for Google's products rather than a great educational course. (I have seen some Google ML courses that were basically just showing you how to use Google's pre-trained models in your apps)

The NRU one has the downsides that I'm not sure of the quality of the course and perhaps it won't be as useful but at least they don't have an incentive to tie you into certain tools.

Has anyone taken any of these courses (or any similar) and can recommend/review them?

After ML course by Prof. Andrew Ng on Coursera - ML by Uni of Washington or Advanced ML by HSE

Hi Everyone,

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I am kinda new to reddit and as I know our ML community is as strong as the whole community on the platform,

I would hope guys could give me some advice on what to do next after I have done this course,

Now my plan is surely to proceed with the infamous Deep Learning one,

For the boost of my skills, however, I plan to take one more course in parallel in order to reinforce my ML knowledge, so I would like to come up with 3 possibilities:

• Reading and implementing in Python the book ISLR
• Taking Machine Learning Specialization by University of Washington (this one seems more theoretical and to include more content than ML by Standford

https://www.coursera.org/specializations/machine-learning

• Advanced ML specialization

https://www.coursera.org/specializations/aml

From my research, this one is good except the language used???

Looking forward to all the responses, best,

Coursera Advance Machine learning - https://www.coursera.org/specializations/aml

I'm ~halfway through this Coursera specialization and it's been a pretty good experience so far: https://www.coursera.org/specializations/aml

It could also be a good idea to take courses that give you more domain knowledge, or general statistics. Even though I had taken university-level probability courses and had done quite some data science already I still learned quite some new concepts from this course (e.g. how to make plausible claims of causality): https://www.edx.org/course/data-analysis-for-social-scientists-1 It also covers some visualization. (Also one week covers machine learning - I found those lectures a bit disappointing but the rest was quite good.)

Αν θες για machine learning, κοίταξε στο Coursera. Τόσο για τα μαθήματα του Andrew Ng (machine learning, deep learning [deeplearning.ai]), όσο και αυτά του National Research Higher School of Economics. Το machine learning είναι τσάμπα. Τα άλλα κοστίζουν κοντά €44/μάθημα.

Επίσης μπορείς να τσεκάρεις και το fast.ai, που είναι δωρεάν. Και έχει το προτέρημα πως αντί για Tensorflow σε μαθαίνει Pytorch, που είναι πιο εύκολο για αρχάριους imho. Και εστιάζει λιγότερο σε θεωρία και περισσότερο σε πρακτική.

Source: έχω παρακολουθήσει τις πρώτες 3 βδομάδες του fast.ai, και το μισό machine learning του Andrew Ng. Ζαχαρώνω καιρό το deeplearning.ai, αλλά σκέφτομαι τα €€.

Θα έμενα μακρυά από τα e-learning προγράμματα του Καποδιστριακού. Μου φαίνονται πολύ ακριβά και μόνο για εσωτερική (εγχώρια) κατανάλωση.

People generally recommend "Machine Learning" by Andrew Ng on Coursera. It's in Octave, but people have done it in Python:

E.g. this person:

https://github.com/dibgerge/ml-coursera-python-assignments

Could also try this chunky specialisation by some Russian uni:

https://www.coursera.org/specializations/aml?specialization=aml

Like the other guy said, read sklearn documentation if you want to do Python. But instead of reading it as its own thing, I'd say use it as a reference personally. But that's maybe because I learn better with videos.

If you want to try and understand Bayesian shit, look here:

https://github.com/avehtari/BDA_course_Aalto

This guy posted a "super harsh" guide:

https://www.reddit.com/r/MachineLearning/comments/5z8110/d_a_super_harsh_guide_to_machine_learning/

For supplementary videos on understanding a topic, you could look up "Machine Learning Summer School" on YT and search through looking for things you might find interesting.

E.g. 2 videos on Gaussian Processes by Neil Lawrence, below is part 1 (but seems shite according to the comments because of some slide issues, but this is just an example)

https://www.youtube.com/watch?v=U85XFCt3Lak

I suppose it depends what you want to learn. What's your endgame here? Like what is the sort of job you're after?

Catalog for Machine learning degree.

I am passionate to learn about deep learning specifically i want to learn about natural language processing....because i want to build my jarvis. Currently i am pursing Btech from India and i am sophomore. The issue i am facing is that i have nobody to guide me and i get distracted very easily. Following my interest i started deeplearning.ai specialization from coursera. Now that i have completed second course i feel like i can not recall many of the things which was taught to me. Therefore now i have decided to make a catalog of course and to start from basics covering all the way to advance. I have also started competitive coding because i love problem solving.

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I am listing few courses which i found are useful, can anyone please develop a catalog so that i can focus and following to become machine learning engineer.

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Foundation courses

1. https://in.udacity.com/course/programming-foundations-with-python--ud036
2. https://in.udacity.com/course/intro-to-data-science--ud359
3. https://www.coursera.org/specializations/data-science-python (Specialization)
4. https://www.coursera.org/learn/machine-learning?

Advanced courses

1. https://www.coursera.org/specializations/deep-learning/?siteID=EBOQAYvGY4A-CZk7TATLvBfdZnDu2EmtDg&utm_content=3&utm_medium=partners&utm_source=linkshare&utm_campaign=EBOQAYvGY4A
2. https://github.com/simoninithomas/Deep_reinforcement_learning_Course
3. https://www.coursera.org/specializations/aml
4. Fast.ai

Ps: I can only opt for the courses which are free or they have scholarship available like coursera.

Hi Rebboc,

>Are there NJ stereotypes that are absolutely true of where you live?

I just Googled a few to see which ones are true where I live (Central Jersey).

1. Jersey people are indeed very proud and love living here. I will say "maybe" as proud as Dominicans. Do you remember the Boston world series where the players wave the Dominican flag? Yes. We are very proud.
2. We are a massive bagel and pizza, eating crowd. I see one shop every corner.
3. Everybody references the place they live off an Exit in the Parkway. My exit is 11, BTQW
4. We struggle to pump gas by ourselves when visiting other states :)

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>Python seems to be gaining some traction in the tech SEO world (JR Oakes's hangout earlier this week is a good example of interest). Are there resources or examples you're excited about that you'd point an advanced Python user towards for SEO purposes?

Yes. I was in attendance and it was a really great job by JR!

Absolutely. Advanced Python users should focus on deep learning and/or automation. Either path will provide plenty of challenging problems.

This is the path I took to learn deep learning.

1. I completed this specialization https://www.coursera.org/specializations/deep-learning
2. Then, I completed this other one https://www.udacity.com/course/deep-learning-nanodegree--nd101
3. I started this one (really challenging) https://www.coursera.org/specializations/aml, but haven't had time to finish it. The NLP module is gold.
4. These courses will give a strong foundation and you can learn new stuff from reading the actual papers. I like this site to keep track of the latest stuff https://paperswithcode.com/
5. Make sure to check my articles! The latest one for Search Engine Journal shows you how to execute JavaScript from Python and create awesome visualizations https://www.searchenginejournal.com/uncover-powerful-data-stories-python/328471/

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>Finally, is there anything up and coming in the SEO or tech worlds that you're looking forward to, whether events, technologies, tools...?

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I'm really excited about text generation and see incredible and fast progress in this area. I gave a webinar for DeepCrawl and more recently shared an example of what is now possible https://www.linkedin.com/posts/hamletbatista_ai-text-generation-activity-6581207906635575296-pSZA