Machine learning has taken off and it’s doing so with fury, bringing new insights to every single industry. If you want to be in demand, this is a skill that will put you in the front line. As intimidating as it may seem, it’s surprisingly easy if you approach it the right way.
Machine learning (ML) is a fascinating practice and field of study. It’s what allowed the introduction of self-driven cars, of robots that can clean your house, the navigation system of drones of all kinds, the recommendation system behind YouTube and Netflix, face recognition systems, hand written recognition, game playing, and lots more.
And because of its incredibly high value and somewhat cryptic nature, it’s an expertise in very high demand that keeps expanding to different areas — which just five years ago would have seemed inconceivable. Through this article, we’ll see different practical ways to approach it.
“Pardon Me … but What is Machine Learning?”
ML is a branch of artificial intelligence (AI). As Arthur Samuel — one of the pioneers in the field — put it, ML gives “computers the ability to learn without being explicitly programmed”. That is, instead of programming a computer (or robot) to do something, you give information and set the framework to let the system program itself.
Freaking fascinating? Yes, but we won’t get into the details of this seemingly impossible thing here, but instead point you to the right places where you’ll be able to find that for yourself.
Before Starting, a Word of Caution
ML is something of an advanced practice, and you’ll need to have not only some foundations in computer sciences, but also be able to code in at least one programming language. Some popular programming languages for ML are Python, R, Java, C, and MATLAB, among others.
1. Start Very Quickly … Like, Really, in Less than Ten Minutes
Sometimes, and for some people, it’s better to just get hands on into something to have a first taste and develop an intuition of what this new art or skill is about, and then dig deeper into some specifics and details.
Google’s Machine Learning Recipes with Josh Gordon is just that — a straightforward and practical approach to ML. Using the Python scikit-learn and TensorFlow libraries, Josh will walk you through very practical examples and down-to-earth explanations behind the very principles of ML.
Here’s the first 7-minute video of the series, introducing a supervised learning algorithm in Python — in just six lines of code!:
The publishing schedule is somewhat irregular, with videos published every month or second month, covering topics such as decision trees, feature selection, pipelines, classifiers: not bad at all for 6-to-8 minute videos that anyone with a little foundation in programming can follow.
2. Take Courses from Top-Notch Universities, for Free
Let’s break it down quickly:
- massive: they have no vacancy limits, and can be accessed by as many people as desired.
- open: anyone can access them, regardless of their age and previous knowledge on the topic, and independently if they’re able to pay for a certification or not.
- online: all you need is a device connected to the Internet; even a mobile phone would do.
- course: these are actual courses with reading materials, practical exercises, and even deadlines.
Let’s see some courses you could start with.
Stanford’s Andrew Ng Machine Learning
Stanford Prof. Ng is a leading researcher on the field of artificial intelligence, and is the person who pretty much started the MOOC spark that would later turn into a fire of knowledge when he first put his Machine Learning online course. The response was overwhelming, with many thousands of people from all around the world taking the course and discussing this topic. He later turned this course into what it is today Coursera, the leading provider of MOOCs.
The course is as fabulous as it is challenging. I remember having spent an hour or so just to read a 5-page assignment scope before I was able to grasp it. So unlike Josh Gordon’s series, this is a little more on the academic side, but with a lot of practical knowledge and advice that will be very useful later on in your ML practices. But it is doable, and the amount of feedback on the forums is truly overwhelming. Mind you, it was among the first MOOCs I ever took, and one of the best.
- Approx. duration: 2–5 months
- Difficulty: high
- Workload: mid-to-heavy
Sebastian Thrun’s Intro to Artificial Intelligence
Also a professor and AI researcher at Stanford (on the field of robotics), co-founder of Google X Lab (the “semi-secret” R&D company behind of Google’s self-driven cars, among other projects), Sebastian is also the founder of a mayor MOOC provider, Udacity. Along with Peter Norvig (Director of Research at Google), he put together the amazing Intro to Artificial Intelligence.
This is pretty much the foundation to all things ML. It’s a lot lighter than Andrew’s course, with its content spread over more units to make it easier to digest, though it’s a long one.
- Approx. duration: 4 months
- Difficulty: intermediate
- Workload: light
Caltec’s Yaser S. Abu-Mostafa Learning from Data
Prof. Yaser is another of the pioneers of putting quality learning material online, making available his Learning from Data ML course on his website, with all of its lectures, learning materials and exams, even before MOOCs were a thing. Later he would package these materials into a MOOC offered regularly by Caltech on edX.
I took this one as well, and I can tell you that you’ll have to do some heavy lifting here. But if you’ve enjoyed Andrew’s course and are hungry for more foundations, this seems like a reasonable next step.
- Approx. duration: 4 months
- Difficulty: very high
- Workload: very heavy (10–20 hours per week)
Other Coursera, edX and Udacity Courses
There’s a very extensive offering of ML and AI courses that you can take for free, not only at Coursera, edX, and Udacity, but at other MOOC providers as well, such as Data Camp — though data science seems to be something of a niche for the three providers we’ve discussed.
3. Get Certified Education, for a Fraction of the Price
So far, we’ve talked free MOOCs. They’re awesome, and you don’t need to pay a cent to enroll in them and start learning. In the beginning, these providers used to offer free certificates or statements of accomplishments, even some of them verifiable online. These programs, however, have been discontinued, so in most cases you won’t get a certificate or any type of credential that you could use to demonstrate your education to a potential employer, or even to another higher education institution.
This may not be a problem if you just want to learn for the sake of it, and even use this knowledge to leverage a successful career as a freelancer, as many professionals already do around the globe. But applying for work can be a different matter, and certs and degrees do ease the way in many cases, so let’s discuss them.
A verified course might be somewhere between $40–$200, depending on the course and the institution. Basically, you pay a premium to get your identity and assignments verified (this is what a verified certificate looks like.) You can find more about Coursera’s Course Certificates and edX’s Verified Certificates. You’ll find they both have a huge offering of ML and data-science–related verified courses, as you can see on this edX search.
Notice that, whether you pay or not, the contents and materials of the course are exactly the same. What you get by paying is the certification that you actually took and passed the course.
Coursera took the concept of verified courses a step forward by grouping some related courses and adding a capstone project to give you a specialization certificate.
Some specializations of interest to us are:
|Big Data||6||UC San Diego|
|Machine Learning||4||University of Washington|
|Recommender Systems||5||University of Minnesota|
|Introduction to Robotics||6||University of Pennsylvania|
|Probabilistic Graphical Models (PGMs)||3||Stanford University|
Coursera Master’s Degree
Coursera’s Master of Computer Science in Data Science (MCS-DS) is an actual, official master’s degree issued by an accredited university. Topics in the program are heavily ML-related, and include:
- data visualization
- machine learning
- data mining
- cloud computing
- information science
- Institution: University of Illinois at Urbana-Champaign
- Price: $600 per credit-hour for a $19,200 in total tuition
- Duration: 32 hours
edX XSeries and Professional Certificates
edX has an XSeries Program for courses within a single topic, in pretty much the same fashion as Coursera’s Specializations. Such series of interest to us include:
|Microsoft Azure HDInsight Big Data Analyst||3||Microsoft||$49–99 per course|
|Genomics Data Analysis||3||Harvard University||$132.30|
|Data Analysis for Life Sciences||4||Harvard University||$221.40|
|Data Science and Engineering with Spark||3||UC Berkeley||$49–99 per course|
edX MicroMasters and College Credit
You also have credit-eligible courses, which are not only verified, but may also serve you to claim for credit towards your B.S. or master’s degree. There are, naturally, a lot of details in the fine print, so you’ll have to do some extra research.
edX MicroMasters are precisely in this vein. Here are some interesting ones (costs are higher here, as you also pay hours of tuition towards a degree):
|Artificial Intelligence||4||Columbia University||$1,200|
|Big Data||5||University of Adelaide||$1,215|
|Data Science||4||UC San Diego||$1,260|
|Robotics||4||University of Pennsylvania||$1,256|
A nanodegree is something of degree, issued by Udacity. While Udacity isn’t itself an accredited educational institution, they went to great lengths to partner with tech industry leaders to deliver the most market-targeted education possible — in other words, to prepare you specifically with the skills that the labor market is demanding right now.
And we’re really talking big names, here: Google, Amazon, IBM, Nvidia, Mercedes-Benz, DiDi, AT&T, among many others. And Udacity’s partners not only co-design the study programs, but even have hiring agreements with Udacity!
Udacity and their partners even go as far as to publish estimated salary figures:
|Artificial Intelligence||6 months||$59.4K to $250K|
|Machine Learning||6 months||$38.7K to $212K|
|Robotics||two 3-month terms||$42k to $156k|
|Self-Driving Car||9 months||$67.8K to $265K|
Get a job or your money back!
In fact, the ML nanodegree is part of the Nanodegree Plus program, which is probably one of the most reckless innovations in online learning: you study and graduate, and if you don’t get a high paying job, Udacity refunds your tuition! Unbelievable.
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