Data is the new oil. And Machine Learning is the fire. Whoever controls these two will control the world.
No, the above is not some pompous phrase picked up from a dystopian novel.
It’s a reality.
The new world order is all about collecting vast amounts of relevant data and processing it into actionable insights — something the human race hasn’t been able to do in history. It’s the kind of technology that allows a country to get ahead of the others, and eventually, rule the world.
As a result, it’s being taken very, very seriously by the progressive nations of the world.
A lucrative career choice
International intrigue aside, data science and machine learning is a hot new field with an incredible opportunity. Demand is off the charts (to put it mildly), and there aren’t enough data scientists around. Not even mediocre ones.
It’s like we’ve suddenly discovered many new inhabitable planets, and there aren’t enough people to move them to. I could go on and on and sound like a broken record, but I think this infographic does the job much better:
So we see that salaries start at $50,000+, and for managers, can shoot well past $250,000.
And not just that, the average person on this planet will be generating 1.7 MB of data per second. That’s 3,500+ TB of data over the entire lifetime — more data than we know how to handle as of now, let alone use for analysis. To say that the future is bright would be doing a disservice to this magnificent new pasture.
Are data science and machine learning hard?
From my experience, the answer is both “yes” and “no.”
Artificial intelligence (and by extension, machine learning), is the hardest thing to do if you’re inclined to get into research and push the envelope. For such work, even a Ph.D. in computer science and mathematics is not enough. But then, the average person has neither the ambition, not the time for such a pursuit.
On the other end is what I’d call Applied Data Science and Machine Learning.
That is, you take existing tools, techniques, and algorithms and apply them to solve some real-world problems. This part requires dedication, perception, and creative thinking (and knowledge of some simple math concepts, which are quickly learned), but regarding true “technical” knowledge, it is much more lenient than what the job of a software engineer calls.
In other words, it’s not a cakewalk, but going by the reward to effort ratio, is one of the best investments out there.
Now that you’ve hardened your resolve to become a data scientist and machine learning engineer, let’s start exploring the best options out there.
Machine Learning (Google)
Not many people are aware, but Google has an extensive, highly practical, and free course on Machine Learning. According to the company, it’s part of their commitment to advancing AI/ML technologies and keeping the knowledge in the open.
The best thing about this course is that there are no prerequisites, but do gear up to spend extra time exploring the statistics concepts on your own.
I mean, it’s not needed, but if you have zero backgrounds in advanced statistics, the explanations in this course may not be enough. Another catch is that this course introduces Machine Learning via TensorFlow, which is an ML implementation developed by Google. So, in a way, Google aims to promote its APIs for Machine Learning, but considering the value offered by this course, I don’t see how that should be a stumbling block.
If anything, TensorFlow is one of the easy ways to get into ML and enjoys rabid popularity (for a comparison of AI frameworks, see this).
The name Harvard inspires awe, and so does this course.
First things first: it’s not a let’s-get-dirty-quick course where you tiptoe around Machine Learning by writing a snippet here or a script here. This course is a severe fire baptism that demands hard work and a significant investment of time.
The course comes with free videos, code (hosted on GitHub), and solutions to lab exercises, so practically, you aren’t restrained by anything if you want to take it.
You…I am not kidding.
I’d say working professionals with decent math education, even though they might not be into math anymore (the habits of inference and proof are the most necessary thing). But once again, please be warned: you might think you’re good, but this course will feel like having hardened nails for breakfast — the practice problems are challenging enough to make you weep, but then, that might exactly be the thing you’re looking for!
Walk into a bar filled with data scientists and ask who Andrew Ng is, and you’ll get a beating of your life.
In the circles of data science and machine learning, Andrew Ng has achieved a godlike status, thanks to his exceptional course on Coursera — Machine Learning.
And if you doubt Andrew Ng’s credentials, I’ll let this speak for itself:
It’s a paid course, in that it’s part of Coursera’s pricing plan, but financial commitment and determination are not the only prerequisites. This is a long course as Andrew dives deep into the math behind all things ML and dissects popular algorithms. But thankfully, it’s a complete course, and you’ll be guided step by step into the darkest depths and brought back.
I highly recommended, mainly because flaunting the completion certificate of this course has become a thing today!
Applied Data Science
Specializations on Coursera consist of a series of courses that aim to take you from zero to proficient in a particular concept. If you’re looking for a complete, serious yet friendly course on Data Science and Machine Learning with Python, I can’t recommend this specialization enough.
At the end of the course, you earn a certificate.
DataCamp offers plenty of data science courses, which also include several skills and career tracks. From data manipulation to machine learning, you will gain career-building data scientist skills in Python and R that will help you succeed in the field of data science.
With the byte-sized content of DataCamp, you can learn at your own pace. These courses provide you with hands-on experience through which you will advance your data science skills.
You can start with the free version and evaluate the course looking at the first chapter.
Learn from MITx, Harvardx, IBM, RICEx, UCSanDiegox, and GTx on the edX platform.
All of them have a comprehensive curriculum that helps you possess data scientist skills. These programs are best suited for those who have statistics or computer science background.
If you are not looking for a program, you can choose an ala-carte. On edX, you will find more than 200 courses related to data science, which covers Python, R, Excel, probability, statistics, machine learning, data visualization, and many more.
Codecademy is another platform which is one of the best systems out there that helps you learn to code. They believe in “Learn by doing” and have plenty of practice projects and tests on their platform.
The data science course offered by Codecademy includes SQL, Python 3, NumPy, Pandas, Matplotlib, Scikit-learn, and many more libraries.
The whole career path container 26 courses which are more than enough to help you become a successful data scientist.
This data course:
Gives you in-depth knowledge of data science
Provides an easy to follow a roadmap
Makes you job-ready by helping you gain enough practical experience
Below are the features/deliverable of this course:
25 hours of on-demand videos
Full Lifetime access
13 articles and five downloadable resources
Access on Mobile and TV
Certificate of Completion
30-Day Money-Back Guarantee
So, if you prefer a budget course, this would be best suited for you to get started.
Would you be interested in learning machine learning from ML experts at Google?
Well, then you need to check out courses on Google AI.
This platform has Machine Learning and Data Science courses and content for students, software engineers, data scientists, and even researchers. These courses are free of cost.
To start with, Machine Learning Crash Course at Google AI should be your go-to course. This is a fast-paced course with a practical introduction using TensorFlow APIs. Below are the details of this course:
Udacity is also a very popular e-learning platform that has a plethora of courses on trending technologies. It has several industry-leading programs built and recognized by top companies worldwide, such as AT&T, AWS, Google, IBM.
One of the programs at Udacity is for Data Science – School of Data Science. This program helps you bag data analyst, data scientist, data engineer, and business analyst jobs. A course on Data Scientist in this program is the crucial one that covers concepts on machine learning, deep learning, and software engineering. You need to have basic knowledge of machine learning to opt for this course.
In case you know python programming but new to machine learning, there is another program on Udacity – School of AI. This program has courses starting from machine learning basics.
This course is a blessing and is my most favorite recommendation on this list if you’re a coder.
I’d say that again: if you’re a coder.
That’s because this course spends no time teaching you the basics of programming. The course description says so in very clear terms (emphasis are original):
We assume that everyone taking this course has at least one year of coding experience. The course uses python as the teaching language, so if you don’t already know python then we assume that you’ll spend the time to learn—for an experienced coder you should find that python is quite an easy language to learn.
So if you already know Python (if not learn here), or can get comfortable quickly, this is the perfect course for the pragmatists who want to build real, usable systems without worrying too much about the theoretical underpinnings of the algorithms.
I might even say it’s for the impatient tinkers (like me!) who hate ceremony and monotony.
And oh, did I mention it’s 100% free and has a great community?!
This was one hard list to compile. Not because there were not enough good sources, but because there were way too many!
Machine Learning is a domain that has literally exploded and is solving hard problems really elegantly, and so there are hundreds of courses online, free and paid, most of them being really, really good. But this can also be a source of confusion, which is why I’ve tried to boil it down to eleven for different types of learners according to their experience level.
I write about, around, and for the developer ecosystem. Recommendations, tutorials, technical discussions — whatever I publish, I try my best to cut through confusion and fluff, and provide actionable answers based on personal experience… read more