Here are some of the best ways to learn the tools to empower you to rule your data
Machine Learning skills are the most in-demand skills in today’s job market. Contrary to what you may hear on the news and in casual conversation, you do not need two PhDs in Statistics and Computer Science to wield the power of machine learning. If you do, that is great, but there are now a bevy of online resources – many of them free – that you can use to increase your skills in DS/AI.
You can find machine learning courses for people with all levels of backgrounds, from no coding experience and a non-technical background, to software engineers who want to learn data science. Pricing and commitment also range, from free to full-on degree programs at an accredited university.
You can see tangible career benefits from taking these courses and implementing them in your daily processes. The whole concept behind machine learning skills is that they take a large amount of data, and allow you to use statistical methods and your knowledge to build algorithms to solve tough problems. These five courses all empower you to learn or improve on the skill of taking data and solving tough problems.
The first machine learning course was offered by Stanford University in 2008 as a set of lectures on Youtube and quickly rose in popularity. Andrew Ng, a loud proponent of machine learning education, went on to found Coursera in 2011 and hosted machine learning classes on the platform. His goal in doing so was to provide access for people outside of top university programs to learn these valuable skills.
The course is an 11-week course and has been many people’s entrance into the field of machine learning. Since then, the number of machine learning courses offered online has exploded and the job market places these skills in very high demand. People now take these courses to expand their skill set, change careers, or to improve their resumes for their current employers.
This course is structured like a college course and is offered at certain times of the year where classes of people must enroll to join the course. This course covers basic machine learning theory and applications. Topics include Linear Models, Bias / Variance tradeoff, and Regularization.
Duration: 10 weeks
Time: 10 – 20 Hours per week
This course is great for those of you looking for an introduction to machine learning, and have a preference for R. This course is a broad overview of some of the most important concepts in machine learning and does not have a steep coding requirement or learning curve. By skipping most of the math behind machine learning and sticking to the fundamentals, this machine learning course allows you to learn the basics while keeping a broad overview.
Time: 6 Hours of content
This section is for those who have some scientific background, and possibly knowledge of calculus or statistics, and/or some programming experience. These courses are a step up from the beginner machine learning courses, which are best for people looking for an impression of machine learning as a whole. These courses will dig deeper into a specific field or topic within machine learning and can help you sharpen your skills and learn some powerful tools.
The first and possibly most popular machine learning course, offered by Stanford and taught by Andrew Ng. This course dives into the math behind machine learning algorithms, as well as the code needed to build them. This course’s tools are Matlab and Octave, two uncommon tools for machine learning practitioners.
Time: 56 hours to complete
Offered as a free course, or as a part of their nano-degree in Machine Learning, this course teaches you the full life cycle of a machine learning project. It also touches important statistical methods such as outliers, and feature scaling. This course is well suited for people with some programming or experience working with company data who are willing to add the most crucial skills needed to be a machine learning practitioner to their tool belt.
Duration: N/A / 4 months
Time: 10 weeks
Price: Free / $399 per month
These courses are designed for people who want to learn the science behind machine learning and possibly already have advanced degrees. If you are used to learning in an academic setting or would like to add coursework in machine learning at the college level, then these may be the courses for you.
This course dives deep into the math and statistical methods that power machine learning. For people who have taken high-level mathematics courses and would like to learn the fundamentals of machine learning from the theory, this machine learning course is a great place to hone your skills. It covers advanced topics such as causal inference, Nonparametric regression, differential privacy, and the theory behind some more popular machine learning algorithms such as linear regression and clustering.
This is an online, MicroMasters program hosted by MITx, the organization hosting MIT online courses. As the largest machine learning course on this list, this program offers a certified degree, or MicroMasters in the field of Statistics and Data Science. The course is primarily tailored to people with the goal of continuing their higher education to obtain a Master’s or Ph.D. at MIT or other universities. This course counts towards those programs and has the benefit of being offered completely online. Anybody can apply to start the course, and it takes up to a year and three months to complete.
Duration: 1 year, 3 months
Time: 10-14 hours per week
Never Late to Start Learning
With the widespread adoption of online course materials by the education community, there has never been a better time to pick up a new skill or certification. Machine learning courses are available to people of all levels and price points. Here we have discussed only 5 of some of our favorite courses, but many, many more exist that may better suit your needs. These courses may be suitable for somebody looking to explore this exciting field, brush up on your coding and math skills, or obtain a certification that can help you move to your next exciting role.
If you have the data and a problem to solve, contact us or request a demo to see how Ople.ai’s automated machine learning problem can empower you to quickly build powerful models and derive insights from your data.