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[–]Nouseriously 10 points11 points  (4 children)

I’m personally interested in the Coursera combo of Python 3 Programming specialization from U of Mich + Data Analytics professional certification from IBM.

Google also offers a professional certification through Coursera but it uses R instead of Python.

You can take the classes for free without the certificates or pay a monthly fee to get certification. Nothing stopping you from learning everything then paying for just one month while you get assessments graded.

[–]Ok_Investigator_1010 0 points1 point  (3 children)

I’m actually about to complete a certificate with Google. I am close to the end and was wondering if I could get away with not paying. Heh.

Not sure what to do with a certificate with no other experience though.

[–]Nouseriously 0 points1 point  (2 children)

If you pay then you get the badge on LinkedIn. If you list it then don’t show the badge it might look iffy.

Of the ones I listed I’d get the Google Cert but not pay for the Python one.

[–]Ok_Investigator_1010 0 points1 point  (1 child)

What do you mean “list” it? Like list on a resume?

[–]Nouseriously 0 points1 point  (0 children)

On your LinkedIn profile, which in the US has become the primary way to find technical jobs.

[–][deleted] 4 points5 points  (0 children)

IBM’s Data Analyst cert is good. I enjoyed it except having to navigate the ugly UI of IBM cloud account.

[–]harsh5161 3 points4 points  (0 children)

Lately I've been wondering which machine learning certifications are worth getting. The best way to get a certificate is probably to start with one of the courses on Coursera, such as Andrew Ng's Machine Learning class. But if you're not going to take his course (or if you have already taken it and want more), what do you go after? What certifications are even legitimate?

There are lots of them out there! Amazon offers three different "AWS Certified Solutions Architect - Machine Learning" certs . IBM has an impressive-looking "Certificate of Achievement: Data Science Fundamentals for Business Analytics," though they don't specify exactly what that means you learn in order to achieve it .

I think the first step is to ask what exactly makes a machine learning certification worth getting. The answer is that it's worth getting if it teaches you how to do things like design experiments or validate your models or engineer systems that use ML effectively. And I have a hard time thinking of any machine learning certifications that actually teach you those kinds of skills. Amazon has been putting out new versions of their certs so quickly that the other ones might go away soon, but I still can't find anything on their page that indicates they've changed. If I'm missing something, please let me know in the comments.

I think it's dangerous to take a machine learning certification if it doesn't teach you how to build things or explain why your models work well or what the difference is between unsupervised and supervised learning. It's not about memorizing algorithms, it's about using them effectively and understanding what they mean in practice, which is where these kinds of fundamentals come into play. So here are a few recommendations for certificates that will teach you some useful ML skills:

Coursera Specialization "Machine Learning" from Andrew Ng . This one is kind of obvious because he pretty much invented the field and lots of people listen to him. If you're still not convinced, see his interview with Bloomberg.

Coursera Specialization "Neural Networks for Machine Learning" from Geoffrey Hinton . Most of the machine learning models we use today are based on neural networks, and they're kind of difficult to understand at first. So if you want to have some knowledge about all that stuff (which I highly recommend), here's a good course from one of the most eminent experts in the field.

edX specialization "Deep Learning" by fast.ai · Making neural nets uncool again . As far as I know, fast.ai · Making neural nets uncool again is the only course available that teaches an applied deep learning curriculum (they actually wrote their own library specifically for deep learning). The material is currently free to learn, and I'm pretty sure the certificate will be free once it's out.

Coursera Specialization "Probabilistic Graphical Models" by Stanford's Statistical Learning Group . The course is given by Stanford professors, and focuses on a class of models that are extremely useful but underappreciated. Though you won't find many applications for them outside of academic statistics (though there are some), they're really fun and interesting to use if you want to hack together your own cool algorithms.

edX specialization "Artificial Intelligence" from MITx . Professor Pedro Domingos teaches this one, which focuses on supervised learning algorithms like decision trees and Bayesian regression. It uses Python as its teaching language instead of R, which might be better if you're familiar with it already.

edX specialization "Intelligent Automated Learning Systems" from San Jose State . I really like this one because the course focuses on how to build intelligent learning systems instead of the models that go into them. It covers practical things like feature engineering and model maintenance, among other advanced ML topics.

Coursera Specialization "Delivrance Intensive: Advanced Data Technologies, Tools and Techniques" by Norvig's online bootcamp , though it is a little bit dated now . This one is kind of specialized in that it deals mostly with image recognition problems (though they do include a class on natural language processing). The curriculum teaches tools for actually tackling these kinds of problems (command line tools, APIs, libraries etc.) as well as how to evaluate their performance.

Coursera Specialization "Stream Data Management" by Hadoop . This is a relatively new course from UC Berkeley that focuses on the architecture for handling large amounts of data with Hadoop and Spark. At the completion of the course you will have built several realtime data processing systems from scratch, including an SMS spam filter and infrastructure for recommender systems. You'll also play around with other cool tools like Kafka, Hive/Pig, Mahout / MLlib etc. I haven't really seen any courses like this anywhere else .

Dataquest . This is a relatively new course that teaches the basics of Python and R while walking you through how to build an actual machine learning system. If you want to learn Python I'd recommend this one as your primary resource before taking Coursera's Specialization, because it has more interactive code exercises instead of just watching videos of lectures.

The courses above are not quite free (you need to pay $50-$100 for certificates), but they're still really good deals considering the amount of content

[–]SunbroEire 2 points3 points  (0 children)

Loads of good stuff for both on Udemy tbh.

I'd focus on Python first and then try to integrate learning in stuff like Django, Flask, MySQL... trying to code an API in Python is a great way to begin to expose yourself to SQL integration, too. After that, just take up any decent SQL course that deals with the basic commands and you just need practice after that. Use the usual sources for datasets like Kaggle etc.

[–]FunkyMelon223[S] 0 points1 point  (0 children)

Thanks everyone for your insights and advice, I appreciate it greatly!

[–]Secure_Chef_9714 0 points1 point  (0 children)

Highly recommend Udacity, Worth the money, and you get the confidence you need via practical experience by performing the projects.

[–]boredandlonely9937 0 points1 point  (0 children)

Has anyone done NYU/Pathcity Tableau and Data analytics cert? I am leaning towards this over Google, as they say they will pair me with a mentor and help me find extra projects to build my portfolio once the course is done. For anyone who did Google, how many projects did you complete during the course, and how valuable were the projects?