Machine Learning Online Courses
Where can you find not-so-common, but high-quality online courses (Free) for ‘advanced’ machine learning Cource and artificial intelligence?
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Why this Machine Learning Cource is Best ?
Many young professionals, who have started their journey into data science, and Machine learning cource , face a common problem — they have completed one or two basic online course, done some programming lessons, put up a couple of projects on Github, Machine Learning Courses ,and then… then what?
What do I mean by ‘advanced’ Machine level course?
Advanced’ is a relative term. It is best to have a baseline to explain the word in this context. Machine Learning Courses Fortunately, we almost have a gold standard when it comes to ML online MOOC — Prof. Andrew Ng’s Coursera course (the original one, not the Deeplearning.ai specialization).
Therefore, by ‘advanced’, in this article,Machine Learning Courses I allude to two characteristics, which need to be present (not necessarily simultaneously) in the courses that will be discussed,
- Significantly more breadth than the Machine Learning Courses aforementioned course i.e. covering more advanced and diverse topics
- Highly specialized focus related to AI or ML
I hope I make it clear that my intention is not to say that Prof. Ng’s course is a rudimentary one. It is still the best introduction to the world of machine learning one can ask for — particularly for beginners. But, after you finish that course, do some programming, Machine Learning Courses feel comfortable about the mathematics concepts, you should build on your base and learn diverse topics.
I just hope that this article can help you do that by listing some free MOOCswith that singular focus.
What is the singular focus for selecting the Machine courses?
AI and ML are hot topics and there is no dearth of free online courses covering those subjects. Although,Machine Learning Courses I have found that there are a surprisingly little amount of true high-quality AI courses out there.
Yes, I am in that camp, which firmly believes that deep learning is not artificial intelligence, and therefore reject the notion of any course, having the word “AI” in its title but covering nothing more than deep learning frameworks in Python, to be classified as an AI course.
So, to restrict my listing to a limited number of high-quality courses, I laid out some simple Machine Learning Courses ground rule or filters.
- I tend to avoid any course with a significant focus on a particular programming framework/tool i.e. no course with a name like “Machine learning with Python…” (some examples or code snippets are fine)
- Following the same logic, the list will have courses with a strong emphasis on the theoretical foundation — this mainly favors university courses over those Machine Learning Courses offered by individual entrepreneurs or companies (e.g. fast.ai, Google, Microsoft, IBM, etc.)
- Similarly, I included Udacity courses which are taught by university faculty or renowned researchers like Sebastian Thrun or Peter Norvig. I did not include their nanodegree references, which I do not find intellectually uplifting.
- I put included two topics which have enormous importance for true AI learning but receive less than usual attention — reinforcement learningand game theory.
- No course focused primarily on data science/data engineering/digital analytics/applied statistics. They all are critically important topics Machine Learning Courses to learn in today’s world but I prefer to separate them cleanly from my focus on pure machine learning and AI for the sake of this particular article.
I believe this focus will automatically curate the list toward high-quality, foundational courses in AI and ML, Machine Learning Courses which can benefit intermediate to advanced learners.
You will be the judge, after all.
Personally, I have not taken all of these courses although I finished a significant portion of them. So, I tried to keep my comments about the courses brief and factual.
The links and references
Without further delay, here is the list.
General machine learning and deep learning
These are courses covering general ML and DL topics.
- Georgia Tech’s “Machine Learning” course on Udacity: This is one of the most comprehensive ML courses out there with coverage of supervised, unsupervised learning,Machine Learning Courses randomized optimization techniques (e.g. genetic algorithm), reinforcement learning, and even introductory game theory concepts.
- The original Stanford classroom version of Andrew Ng’s lectures: This is the full classroom Machine Learning Courses version of Prof. Ng’s ML course at Stanford. Covers foundational topics of ML in depth which are missing from the watered-down online MOOC.
- “Advanced Machine Learning Specialization” by National Research University Higher School of Economics on Coursera: This is a great set of courses (5 in total) Machine Learning Courses offered by Russian researchers. Good coverage of practical deep learning techniques along with foundational concepts.
- “Machine Learning at Scale” by Yandex on Coursera: Covers deployment and scaling up of ML Machine Learning Courses models using MLib/Spark etc.
- “Machine Learning Caltech course”: This was on edX before but since moved to Prof. Mostafa’s home page. The link point there. It is a great foundational course in deep mathematical aspects of machine learning and learning theory in general.
- “Machine Learning Fundamentals” by UC San Diego on edX: A well-balanced course teaching core Machine Learning Courses theoretical and practical concepts in ML with emphasis on algorithmic issues.