Blog: Machine Learning — Modeling the future
Wonder what the buzzwords ML and AI mean? Read on…
Within the period of this decade, we saw a lot of new technologies coming up and Machine Learning and Artificial Intelligence are among the ones on the top list. Its that technology which brought in a whole new dimension of changes in the way we interact with the world of data.
So, What exactly is Machine Learning?
Think of how we learn to ride a bike. When we first try it, we fall down. On the next attempt, we make improvements based on how we fell down the previous time. That is, we learn from our mistakes. On every attempt, we try to improve the way we do it based on what we learned from our previous experience. This means that in some way or other, we create a model of the ideal solution for the problem (In this case, riding bike) in our brain, which we then use in the next attempt.
In the above example, we saw how we learned from our experiences. There, we had 3 major elements — the data (here, how we fall down in each attempt), the model (here, the optimal model we created from our experience which we use in the next attempt) and mainly, the problem that we are trying to solve. In simple words, for every problem we get, we collect data from our experiences and find the optimal solution to it.
Machine learning is similar to human learning. Actually, it’s how we humans tried to mimic the way we learn in machines. We take a problem, collect a large amount of data (Normally in millions of samples), apply some mathematical function upon the data to extract or model the solution to the problem. The so generated solution will not be the precise solution, but it will be close to ideal (optimal) and dynamic solution (that is, it will change with respect to the change in the related parameters). Got confused? It’s ok. It’s just applying mathematics and our problem-solving logics on a large amount of data to find solutions like the one we discussed above (Just consider that we are trying to make a robot to ride a bike and compare to our previous example). Sounds interesting, right? Now let’s see one more simple example.
Think that you are working on weather prediction. You are asked to predict tomorrows weather (if there will be a thunderstorm or not) and you are given weather information of the past 50 years (this is the data we use to train the machine. Let’s assume that it has hourly information about the rain, wind, and thunder for your region). Also, you have wind and rain sensors. Now, this is like a linear algebra problem on a large dataset, where X is the rain status, Y is the wind status and the unknown Z is the thunder status. Then, we train the machine using some machine learning algorithms (of which the core part is some mathematical function) to create a model (here, it may be a 3D graph) which we then use to find the current weather by giving the reading from the two sensors as input. Here, the machine acts like a guy who has experience in weather and knows to predict the weather based on the climatic changes. Woah! That’s the beauty of machine learning, making your own genie using mathematics, data and the ever-increasing computation power of machines.
So, why is it popular these days?
The idea of Machine Learning dates back to the 1900s, but it took more than half a century to come into reality. There are a lot of reasons we could point out. One major reason is the weak computational power of the processors that existed during that time. Also, there was no Internet as we have it right now nor any smartphones and this many users to collect the huge amount of data that is required for training. Apart from all these, there exists a huge investment required for the research purpose, which was then not available because of the lack of market for the products that could have been implemented using ML. These three limits the most important requirements for implementing machine learning — data, powerful machines, investment, and efficient algorithms.
In the modern world, ever since the coming of the Internet and smart devices, we have seen a huge advancement in the area of technology. This made smart technologies among the hottest market anyone could invest in. Due to the ever-growing demand, more and more investments came into the area of Machine Learning and Artificial Intelligence, which lead many companies and researchers to work in this area. Also, the huge set of open-source resources and the easy access to these resources via the Internet made it easy for young minds to try working in this domain. And the result? A whole new world of automated machines, that are smart enough to do several tasks that once only a man could do. We saw machines beating humans in playing alphaGo, helping us in analyzing the interstellar space to unfold mysteries, making self-driving cars, capturing beautiful images and many more that now has an influence on every human in one way or the other. Also, a whole lot of open-source Machine Learning libraries are available, which made it a whole lot easier for anyone with little knowledge to work in this domain. All these came together to make it the hottest domain of this decade.
What all could it do? or How could this technology help us?
Think of a situation where you want to predict how well your product will perform in the market. You have the data of all the previous products, their market performances and the parameters that affected its performance. Using this data, you could train a model to predict the current outcome based on the present parametric values and hence take strategies to improve its market performance. That’s not all. Read on…
Have you seen robots jumping and running through complex obstacles without failing? Wonder how it’s done? Actually, it’s a perfect mix of mechanics, sensors, electronics and finished with the magic of machine learning. They are trained from their own previous experiences of failure or winning on a reward basis (aka. reinforcement learning).
From all the examples we saw in this article, we can say that you could possibly bring wonders to any of your creation using machine learning, which would not have been possible earlier.
It’s in great demand. Yes, you heard it right. Almost all the tech-giants need Machine learning engineers for one purpose or the other, ranging from analyzing business growth to creating self-driving cars and intelligent robots. Just look at the smartphone that you have in your hand right now. The camera, Google assistant, Google photos, Spotify, Flipkart, Amazon app, all these runs on top of Machine Learning. Camera apps these days use AI to detect scenes and optimize the image to make it look professional. Google photos use ML to detect faces in the photos from your gallery and classify it. Spotify uses the list of the music you listen to suggest new music based on your taste. Flipkart uses it to suggest products based on what you are in search of. Likewise, most of the companies and applications that you come across every day have people working on machine learning.
Seems pretty interesting, right? Wish to learn it?
Where to learn?
The best and easiest way to get started is to just Google “Machine Learning” or “Machine Learning tutorials”. Google gives you the best suggestions.
You could take a course on learning platforms like Udacity or Udemy. That way, you could get certified at the same time. Or just Google “MLCC” to get some free content on Machine Learning by Google.
You could also find a lot of research papers and open-source guides on machine learning, even in your own language.
Learn from engaging with communities dedicated to machine learning and AI like the KeralaAI Initiative, Google Developer Groups, School of AI, etc.
Learn from attending workshops on Machine learning or attending a machine learning crash course.
If you are in Kerala pursuing Engineering, you could take the Machine Learning course by ASAP, which is a one year program, focusing on core machine learning, mathematics, and algorithms. That way, you could get a deeper understanding of the field.