Machine learning certifications are picking up a lot of steam for job-seekers and those looking to make a lateral move from their field. Data analysts, business analysts, and other professionals who use data are finding themselves intrigued by the premise of machine learning, but may not have the time or resources to devote themselves to another college degree. For those looking to obtain a machine learning certificate, here are a few things that you should consider when choosing one.
What do you actually learn with a machine learning certificate?
Some places will offer a machine learning certificate that’s rather barebones and focuses more on receiving the certificate than actually learning new skills. Machine learning isn’t just writing algorithms all day, rather it involves knowing math, statistics, data analysis, and more. A few things you need to know include:
Understanding the basics of machine learning (i.e. Machine Learning 101):
This includes knowing what really goes into machine learning, aka the math, algorithms, statistics, and general expectations of what the field entails.
Choosing a language (likely Python, maybe R):
Python is the most popular language that many data scientists use for machine learning, however, some carve their own niche with R or another language.
Learning data wrangling (with SQL):
Data wrangling means turning your existing data into something usable, and SQL’s the go-to for data cleaning and processing.
A foundation in mathematics (calculus, linear algebra, etc.):
Machine learning is all built on top of mathematics, so knowing the core skills will go a long way. Plus, knowing math is a good skill across all forms of data science, data analytics, and data engineering.
Knowing some basic statistics:
Statistics is a universal skill that finds a lot of use with applications in a business setting, so being able to apply broader knowledge in the realm of data science will help in a job setting.
Data analysis and data discovery (such as using Pandas for data discovery and transformation): Similar to data wrangling, finding and analyzing data itself are core skills in data science, and will definitely be used by a practicing data scientist.
Both types of learning are useful, have their own advantages and disadvantages, and will be used pending on the situation. It’s not a one or the other like R versus Python, so being able to perform both supervised and unsupervised learning is a necessity.
Cost – is it Affordable?
This is where it gets a bit grey. A 2000 may be a bit much – and at that point, it’s a semester in college in some places. This requires some more thinking, such as looking at what you get out of it, looking at the reputation of the certificate provider, finding reviews of other people who’ve taken it, and so on. And be realistic about it – what’s it worth to you? If you have a few bucks to throw around, then take a chance. If you’re getting a certificate just for fun, then maybe you don’t need to break the bank.
Length – Can You Finish it?
The length of a certification program is often a selling point for many, as they’re faster to complete than a degree. But, there’s still a difference between a one-week certification and a six-month certification. At that point, you need to consider your end goals. Do you just need the certification for your resume? Or are you really looking to beef up your subject matter expertise? Factor that in with your schedule, budget, and how much of an attention span you have and take it from there.
Do you get any badges/tokens/achievements?
Proof of progress and completion go a long way. By getting completion badges, you can use those on your resume or LinkedIn profile as well to prove how much work you’ve put into improving your skillset. What may seem like a simple gamification tactic is actually another signifier of progress and growth.
Continued learning – do you learn anything else?
Some machine learning certifications don’t stop once you get the proof of completion, and why should you stop learning? Machine learning is bigger than what any individual certification can provide, and then there are subfields such as deep learning, natural language processing, MLOps, and other fields where you can specialize in. See if the machine learning certification you’re looking at gives you the chance to keep learning even after you obtain the certification, such as more courses on said specializations.
Check out the Ai+ Machine Learning Certificate for all of this
If you want a machine learning certificate that’s comprehensive, affordable, and allows for continued learning, then check out the Ai+ Machine Learning Certification. In only 8 weeks, subscribers to the Ai+ Training platform can watch a series of on-demand videos that will cover everything you need to know to get started with machine learning, including a general machine learning primer, Python fundamentals, SQL, mathematics and statistics for data science, Pandas, and supervised and unsupervised learning.
Subscribers to the Ai+ Training platform can watch unlimited live and on-demand videos as well, which dive into everything from introductions to NLP to advanced deep learning techniques. Some upcoming live training sessions include:
- Time Series Forecasting (With Python): July 13th | Marta Markiewicz | Head of Data Science at Objectivity
- Reinforcement Learning for Game Playing and More: July 20th | Amita Kapoor | Associate Professor at the University of Delhi
- Bayesian Inference with PyMC: August 17th | Allen Downey | Computer Science Professor at Olin College
Ready to move to the next level of your career? Sign up for Ai+ Training and start the machine learning certificate process today.