|Coursera Professional Certificates In AI|
|Written by Sue Gee|
|Tuesday, 08 October 2019|
Machine Learning, Deep Learning, Neural Networks - not only do we trip over them at every other turn, the message that they are the way of the future is constantly reiterated. Coursera, in partnership with IBM now has two Professional Certificates for those who want an AI credential or to embark on an AI career.
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Coursera recently launched several Professional Certificates with the strap-line:
The content for Professional Certificate courses has been created with industry partners and IBM is on board not only for AI but also for Data Science, see MasterTracks and Professional Certificates in Data Science on Coursera, where we outline the IBM Data Science Professional Certificate.
There are two choices when it comes to AI, IBM Applied AI Professional Certificate at Beginner level and IBM AI Engineering Professional Certificate at Intermediate level and both are motivated by the same claim:
AI is expected to generate over 50 million net new jobs in the next few years alone, World Economic Forum (2019)
Both of these programs are self-paced and charged on a subscription basis, after a 7-day free trial, and financial aid is available to those who need it. According to the headline information they could take as little as two months to complete if you devote two hours per day on average to them. A more realistic expectation is provided in the FAQs:
The Professional Certificate consists of 6 self-paced courses. Effort required to complete each course is 4-5 weeks if spending 2-4 hours per week. At this rate the entire specialization can be completed in 3-6 months.
The FAQ also gives the information that you can enroll in a single course and can audit it to view the course materials for free. However, this option doesn't seem to be available to all the courses listed below.
The first three courses of the beginner level IBM Applied AI Professional Certificate also constitute the complete AI Foundations For Everyone Specialization and have no prerequisites at all. They are:
The blurb for this course claims:
Chatbots are a hot topic in our industry and are about to go big. New jobs requiring this specific skill are being added every day, consultants demand premium rates, and the interest in chatbots is quickly exploding. Gartner predicts that by 2020, 85% of customer interactions with the enterprise will be through automated means (that's chatbots and related technologies).
If chatbots aren't what you are interested in the remaining three courses in this Professional Certificate open other doors. Indeed, the first of them is shared by the IBM Data Science Professional Certificate and is described as a beginner-friendly Python course will take you from zero to programming in Python in a matter of hours, while the other two further justify the Applied AI descriptor of this credential:
The IBM AI Engineering Professional Certificate is at intermediate level and you need some Python as a prerequisite. It dives straight in with Machine Learning with Python and the next four courses look at other components of the AI engineer's toolkit:
The final component is the AI Capstone Project with Deep Learning (starting 10/30/2019) in which learners will apply their deep learning knowledge and expertise to a real world challenge by using their library of choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.
In brief, after competing this Professional Certificate, you will have acquired a practical understanding of Machine Learning and Deep Learning and will have encountered fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders. You get the opportunity to have hands on experience of popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
To quote from the course blurb:
By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer.
It sounds like an ambitious claim, but with high ratings for the courses that are already available, it looks as though Coursera has found a formula that learners appreciate.
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|Last Updated ( Tuesday, 21 January 2020 )|