In the past few weeks I have been getting quite a lot of request for recommendations on AI, Machine Learning and Data Science related courses and guess what many of them are looking out for free resources! so in this article, I have compiled a list of the best free online courses on AI, Machine Learning & Data Science.

Please note that this courses spread across a wide range of audiences and learning stage so either you are looking at starting afresh or you are looking at programming your own machines and robotics, either ways I believe you would find the resources quite valuable.

  • IBM Data Science Fundamentals – Provided by IBM, this course covers data science 101, hands-on applications, programming in R & open source tools. The total duration is estimated to be about 20+ hours however, beginners may take a little bit longer to complete.
  • Learning From Data By California Institute of Technology – This program is made up of a series of learning videos alongside home work assignments and final exams. It focuses on machine learning and goes in depth with the mathematics mostly matrices and calculus. Maths newbies might find this a little bit tough.
  • Dataquest – Unlike the examples above, Dataquest is an independent online training provider that offers free access to its courses. With endorsements from Uber, Amazon and Spotify, Dataquest seems like a credible platform to get started with studying data science especially when you are not looking at spending too much. They offer three different paths which are data analyst, data scientist and data engineer and also have an optional premium plan.
  • Machine Learning – Columbia University – To get the most out of this course, participants should expect to spend about 8 to 10-hrs per week for a period of 12-weeks to cover all materials and exercises provided by the course. It is available for free online with an option to pay for certificate.
  • Fundamentals of Deep Learning for Computer Vision by Nvidia: This discipline of machine learning covers how to build computers that can see by processing visual information the same way our brains do. While the entire course might take about 8-hrs or more to complete, participants have the option to study at their own pace. At the end of the course, the final assessment covers building & deploying a neural net application amongst other technical fundamentals.
  • Google Machine Learning Crash Course: This course focuses on deep learning and the design of self-teaching systems that can learn from large, complex datasets. Suitable for interested participants looking at to put machine learning, neural network technology to work as data analysts, data scientists or machine learning engineers as well as enterprising individuals wanting to make use of the plethora of open source libraries and materials available.
  • Helsinki University – Elements of AI: Original available only in Finland as part of the government’s initiative to educate it’s population on AI, only recently was it made available to the world. The course has been structured for anyone looking to understand what AI does, what it can be used for, without getting involved in the underlying mathematics and statistics.
  • Deep Learning For Self Driving Cars By MIT: Originally taught at the institution for the first time last year, all resources and training materials are now available online for free. However, there will be no certifications for learners. The course covers the use of the MIT DeepTraffic simulator, which challenges students to teach a simulated car to drive as fast as possible along a busy road without colliding with other road users.
  • Learn With Google AI: Provided by Google, this course covers the fundamentals of Google AI, offers instructions on applying AI and ML to social, environmental and humanitarian challenges. Although materials are still being added, it already contains a Machine Learning with TensorFlow (Google’s machine learning library)
  • The Open Source Data Science Masters: Although there is no certificate for this course, this program is great for someone looking to access a wealth of information on data science. Subjects covered include natural language processing of the Twitter API using Python, Hadoop MapReduce, SQL and noSQL databases and data visualization. It also includes a grounding in the algebra and statistics needed to understand the fundamentals of data science.
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