Learning is a process of acquiring new or modifying existing knowledge, skills, behaviors, values or preferences. When you are a newborn child the process of learning starts and continues until the complete lifespan. There are only two stages unlearned and learned, learning is a one-way bridge between the unlearned to the learned stage.
Similarly, computers like humans also follow this learning process. Information Technology has changed the way of living making the lifestyle more easy and comfortable we could ever imagine. So how this works? Have you watched any sci-fi movie where the robot becomes self-aware using artificial intelligence, I guess you got two-three names in your head. So how does it happen? The answer is very simple through Machine Learning (ML) and Artificial Intelligence (AI).
ML and AI what is the difference between them aren’t they the same?
No, they aren’t the same the basic difference between them is AI is the broader concept of machines being able to carry out tasks in a way that we would consider “smart” and ML is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves. ML is an application of AI that provides systems the ability to automatically learn and improve from experiences without being explicitly programmed.
So how Machine Learning works?
ML helps in prediction and forecasting, it follows data mining to extract meaningful patterns out of the data warehouse. The data warehouse is a collection of historical data. ML example can be a rainfall forecasting system where the system has all the recorded data of previous years rainfalls. We can also model conditions like – if the temperature is greater than 35-degree Celsius, humidity is in the range 70 to 95, etc. And feed these ‘features’ manually to the system. So based on this, the system will do the mining to analyze the patterns and predict the future rainfalls.
So, what after Machine Learning (ML)? It is Deep Learning (DL).
The concept of deep learning has been around for two-three years now. But these days DL is getting more attention, DL is a subset of ML.
In DL machines think like human brains using Artificial Neural Networks. For an instance look at these two shapes,
Our human brain process it in a conceptual manner looking at the four sides and recalling properties of Square and trapezium. This happens so fast that we do not realize that our mind processes this in such a way. Similarly, in DL a bigger task is broken up into smaller abstract tasks, DL do this at a very broad and large-scale basically an end to end approach.
Let us take another situation to show how ML is different from DL if we take the below example
Image source: Google
ML takes the problem and breaks it into parts, solve these parts individually and combine the results to find a solution to the problem while on the other hand in DL the approach is end to end. In the above example of multiple object detection and location detection in an image. ML will divide the problem it into two steps, object detection, and object recognition. Firstly in ML, a bounding box detection algorithm is used to skim through the image and find all the possible objects. Then of all the recognized object, object recognition algorithm is used to recognize relevant objects. On the contrary, in DL once the image is passed it would give out the location along with the name of the object.
Conclusion
Firstly all the companies who want to gain the competitive advantage in the market should inculcate ML in their business. ML should not only make companies to customize user experience but also to offer something very unique like YouTube, my youtube’s feed will always be different from yours. These days speech recognition, android apps, drug discovery, maps, image understanding, planning, and forecasting are the key areas where it is exploiting an opportunity. But the possibilities and applications of ML and DL are endless.
Learning is a key to unlock new business opportunities to gain the competitive advantage in the market. Now businesses through Machine learning can do a better understanding of the customers and explore revenue opportunities.
The most common Machine Learning applications for businesses can be customer lifetime value, customer segmentation, dynamic and variable pricing, brand loyalty, Up-selling and Cross-selling through recommendations.
