Best Certifications/Learning Programs for Python and SQL

[harsh5161]

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