Why Data Science and Machine Learning
Summary : Data Science and Machine Learning affects you more than you think it does. It is just a matter of time before many jobs are lost to automation and many more new jobs created for the ones with the right skills. We are in the middle of the 4th industrial revolution – and don’t miss the bus. On this page, we are trying to convince you to join this huge change.
- Machine Learning examples in the real world
- What is in it for you
Machine Learning examples in the real world
We have been exposed to Machine learning without realizing it. Just think of these examples
- Amazon product recommendations
- Netflix movie recommendations
- Google ad targets
- Chat bots
- Self driving Tesla cars
- Face detection
and the list goes on and on. These are just examples in the consumer world. Machine learning and data science is used heavily behind the scenes as well
- Paypal fraud detection
- Cancer detection based on MRI scans
- Algorithmic trading
What is in it for you
Data Science and Machine Learning have become essential skills in a lot of domains. Can you imagine these scenarios 5 years ago ?
- An ERP Finance consultant, configuring their system to match inbound vendor payments to its original invoice based on a check scan.
- A software engineer, trying to work on software signatures rather than source code to identify malware in an antivirus company.
- A programmer writes a program to detect cancer based on his Blood pressure, age, sex and other simple parameters, rather than having the patient go through an MRI and let a radiologist determine cancer.
The point being, Machine Learning ( ML ) has become so pervasive that is touches every domain and everybody’s lives in many more ways than we can imagine. Sure, everybody is an expert is his or her own domain – but is there any excuse for a doctor or a lawyer to say they don’t know how to draft an email on a computer. Sounds silly, right ?
In the same way, ML is going to be the de facto means of pattern recognition in the future. And we would have to leave conventional thinking aside and think of possibilities from a new angle. Sounds like we are trying to sell you something right ?
I don’t blame you – but think of the MRI example above. Of course the radiologist knows more than the computer. But there is something fundamental that is changing here – use of older data to draw new conclusions. While the radiologist uses his domain knowledge to predict cancer, ML uses data from thousands of scans and combine the subject specific parameters to predict cancer. You can’t produce a radiologist on demand – but a computer ? Sure you can.
For example, ML can create this
from this .
As a consumer of ML solutions, you need to be aware of the brave new world of possibilities. If there is a pattern and you have enough data, ML algorithms can be used to predict the pattern. You need to be aware that youtube or netflix is using your watch patters to predict videos that you might like. You need to be aware that the data you provide to google is used in myriad ways to provide relevant search and ad content.
If you are working in a warehouse, you need to understand how machines can one day replace your job stacking those racks or how drones are using AI to deliver packages to the consumer.
On the other hand, if you are into programming, machine learning is nothing short of magic. There is no need to write IF, THEN, ELSE logic to create complicated rules that continuously keep changing. If there is a patter, let ML do its job. All you have to do is wrap your logic around it and look like a cool guy.
For example, if you were writing an Android or iOS app to recognize what’s in a user uploaded image and tag it accordingly. What would you do ? Well, you could use Google Vision API. For example, the following image has
the following characteristics. How cool is that ? All you have to do is call the API and send the picture along.
Say you are that radiologist, what do you do ? You have a much bigger role to play. A student is just as good as their teacher. You are the teacher, and the computer is your student. Computers can recognize patterns based on ML and Deep Learning (DL), but it still needs oversight. You are responsible for providing the correct inputs, validate the results and optimize the way ML works.
With such a big technology change comes tons of job opportunities. You must have heard that Data Science is the sexiest job of the 21st century . And would you have ever imagined that there would be a “Chief Data Scientist” job in the United States Office of Science and Technology. Here is a snapshot of the AI ( Data Scientist, Machine Learning, Deep Learning etc ) jobs in the US in Oct, 2017.
As you can see, that is a pretty significant chunk. The bulk of the recruitment right now is happening in the tech giants like Google, Facebook, Twitter, Microsoft, Paypal etc.
And AI would be creating 58 Million new jobs by 2022. And if you start now, you would have a pretty good chance at getting one in the next 6 months.