In the past year there's been a bit of a careers and job scare when it comes to artificial intelligence, automation, and related technologies. Big consulting firms have conducted studies about the future of jobs and whether they will be lost to artificial intelligence.
The consensus is that jobs will be lost, while some jobs will be created.
"The development of automation enabled by technologies including robotics and artificial intelligence brings the promise of higher productivity (and with productivity, economic growth), increased efficiencies, safety, and convenience," said McKinsey in a study released last year. "But these technologies also raise difficult questions about the broader impact of automation on jobs, skills, wages, and the nature of work itself."
These reports and others have had workers asking the question, "What skills do I need to for the future? What's the best career to pursue?"
LinkedIn's 2017 US Emerging Jobs Report, published in December, noted that the demand for data science skills has been on the rise for several years.
"Data scientist roles have grown over 650 percent since 2012, but currently 35,000 people in the US have data science skills, while hundreds of companies are hiring for those roles - even those you may not expect in sectors like retail and finance - supply of candidates for these roles cannot keep up with demand," the report said.
What's more, the report cites the growth rate for machine learning engineer jobs to be higher than that of any other job over the last 5 years.
Just what is a machine learning engineer, and how can you get the skills needed to perform this job? The LinkedIn report also looked at the career paths of professionals who are currently employed as machine learning engineers. The top jobs leading to that the machine learning title are software engineer, research assistant, teaching assistant, data scientist, and system engineer. The top skills for that job title are machine learning, research, algorithms, software, and deep learning.
Experts at the recent AI Summit at Interop ITX 2018 said that to keep up with their own skill sets they attend conferences, read academic papers, participate in professional communities. Cloudera Fast Forward Labs Data Scientist Friederike Schuur said that there is so much material online that people can use to improve their skills. But the panel overall urged caution in signing up for just any old course with machine learning in the title. Not all are created equal.
With that in mind, InformationWeek has put together a short list of 5 free online Machine Learning courses created by some of the top schools in the country. These courses are not entry level. Even the one course that is listed as introductory has some prerequisites. Prerequisites for these courses overall tend to include computer programming and advanced mathematics. But if you are a professional looking to transition to one of the highly paid, in high demand positions of machine learning engineer, here's a selection of online courses that can help you get the skills you need to make the move.
Berkeley, University of California, via edX
This 5-week class will give you the basic groundwork so that you can understand the concepts associated with machine learning. Topics will include two main methods of machine learning -- regression and classification.
This course includes video lectures and video transcripts in English and is estimated to be a time commitment of about 4 to 6 hours per week. It is part of a professional certificate program, and a verified certificate is available for a fee, although the course without the certificate is offered free of charge.
Prerequisites: Although it's billed as introductory, this course does have two prerequisites. They are Foundations of Data Science: Computational Thinking with Python and Foundations of Data Science: Inferential Thinking by Resampling
MIT Open Courseware
This course starts with machine learning algorithms followed by statistical learning theory, and then the history of machine learning and statistics along with Bayesian analysis.
Among the major topics in the course are an overview of the top 10 algorithms in data mining and frameworks for knowledge discovery.
It includes a syllabus, instructor insights and lecture notes, including instructions for installing and working with R, projects, and data sets.
Prerequisites: This course is aimed at introductory graduate students or advanced undergraduates. You need a mathematical background in basic analysis, probability, and linear algebra. You will learn R during the course.
California Institute of Technology
CalTech describes this as an introductory course in machine learning that covers basic theory, algorithms, and applications.
Lectures span mathematical theory, practical techniques, and conceptual analysis. Those who participate in the course can also find particular topics within the lectures in the Machine Learning Video Library associated with the program.
This course includes 18 recordings of live lectures, each about 60 minutes long, along with live questions from students. There are eight homework sets and a final exam, plus a discussion forum for participants.
Prerequisites: Basic probability, matrices, and calculus
Columbia University via edX
This 12-week course covers classification and regression, clustering methods, sequential modelling, and model selection. The first half of the course covers supervised learning, and the second half covers unsupervised learning.
The course itself is offered free of charge, but for those interested in getting a verified certificate of completion, there is a price. This course is part of a micromasters program in artificial intelligence. Other courses that are part of that program cover robotics and animation and CGI motion.
This course is described as advanced, and several reviewers have rated it as hard. It's not an introductory class.
Prerequisites: Calculus, linear algebra, probability and statistical concepts, coding and comfort with data manipulation
Part of the Stanford Engineering Everywhere series of free online courses, this one provides a broad introduction to machine learning and statistical pattern recognition. This is the course frequently cited as the gold standard for free online courses in machine learning. It includes a series of 20 lectures, lecture handouts, review notes, and assignments.
Topics include recent applications of machine learning, such as data mining, autonomous navigation, speech recognition, and web data processing. The course also includes topics such as supervised learning, unsupervised learning, learning theory, reinforcement learning, and adaptive control.
Prerequisites: basic computer science principles knowledge and skills. You should be able to write a non-trivial computer program. You should be familiar with basic probability theory. You should be familiar with basic linear algebra.Jessica Davis has spent a career covering the intersection of business and technology at titles including IDG's Infoworld, Ziff Davis Enterprise's eWeek and Channel Insider, and Penton Technology's MSPmentor. She's passionate about the practical use of business intelligence, ... View Full Bio