When I say machine learning, it reminds me a subject for the B.Tech Mechanical stream. No this is not a session of machine learning or it’s not related to your regular subject knowledge. The topic remains the same but it’s still different. So before we go to the point why should we be bothered about what is machine learning. We should now see what is machine learning actually.
Machine learning is a subcategory or to a say, a type of artificial intelligence (AI) which gives computers, a privilege to learn without being explicitly programmed. It mainly focuses on the development of computer programs that change regularly every time a new data is encountered.
If you observe that, the process of machine learning is very similar to data mining. Since, both the system’s search for data by going through certain patterns. But here the difference is, in data mining the data is extracted for human comprehension, while in machine learning data is used to search for patterns in data and adjust program actions accordingly.
Now coming back to the topic, recently machine learning has been a hot topic around the market. So it’s not just for mechanical or civil students. Even if you are not one of them, you have never worked on it as a developer, you have no idea about it still you are acquainted with the machine learning phenomenon as clients or consumers.
Confused? Okay, so let’s take an example of an online shopping site, say Myntra. Now every time you add a product to your cart in Myntra you will see a list of other recommended products that you may like. How does a site know about your likes or preferences? The site knows because of Machine learning. So machine learning is the evolution of computer programs that can learn and then create their own set of rules based on the given data.
Again, developing any standard application is very different from developing machine learning applications. Here, you just don’t write code for a single issue. In machine learning algorithms are created with the ability to take data and then create their own logic on the basis of data given in the first place.As we saw the Myntra example, there the data regarding customer behavior and sales was used to list recommended products which the customer might be interested in.
We can’t say it as a 1:1 relationship between items in your cart and any other product. Just like something a marketer recommends you purchase together rather than just summarizing all information from existing data, your visits, and all sales and then using that analysis to assume the behavior and regulate that recommendation that is relevant. In this way with new products there are new data as input changing the recommendations results frequently adjusting and improving.
That’s a very valid question, why should one care about machine learning. The thing is, with growing speed of Internet of Things and connected devices, we have access to anything about everything. With this growing data, the need to manage and acknowledge what we know is also growing.
As many companies depend in machine learning, they have has a great chance to work in this field. You can always try to give it a shot as a developer and understand it’s working. Along with understanding how it may enhance and improve the value of a particular product.
Machine learning is a vast field. So to categorize data and information through some measures, the machine learning concept is broadly categorized into four different types. These are as follows:
In this type of learning the training, dataset which includes input with known output results. This is helpful for the machine studies to apply until they are skilled enough to apply the label on its own. It is advised to pursue supervised learning under supervision or proper guide.
For example, you want to create a face detection algorithm which not only detects faces but also provides images of landscape, animals, waterfalls, buildings and so on, with their respective labels till the machine could independently recognize or detect any face with an unlabeled picture.
In this type of learning, the machine differentiates the unlabeled data and classifies it on the basis of various similarities it has detected or observed. A supervision or guide is not required for this type of learning. Let’s take the same face detection algorithm example and continue with that here too.Here what happens is, the machine provides the images as mentioned above but there are no mentioned labels.
The machine can cluster images on the basis of some shared features. Like the sharp lines of a city-scape differentiates it from the round shaped face. But the machine couldn’t say that the round face image is a face. The unsupervised learning programs are generally used to detect groupings within the data sets that might be very difficult or in fact, impossible for a human eye to see, detecting is far impossible.
The semi-supervised learning can be stated as a mixture of both supervised and unsupervised learning. This type of learning is used when the given data is very large but only some of them are labeled data. As supervised learning techniques can be used to predict labels for it.
This type of learning is of great use in situations where the labeling cost is very high as compares to the unlabeled data, that is pretty much inexpensive. According to many machine learning researchers the unlabeled data, when used in conjunction with a few labeled data yields in considerable improvement in learning accuracy.
This category of machine learning is motivated by behaviorist psychology. It deals with how software agents decide to take actions in any situation in order to maximize some notion of cumulative reward. Due to its generality, this issue is studied by many other disciplines like game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics, and genetic algorithms.
The reinforcement learning is very different from supervised learning as there accurate input/output are presented neither are sub-optimal actions explicitly corrected. It highly focuses on on-line performance, that includes finding and maintaining a balance between exploration that is, into uncharted territory and exploitation that is of current knowledge.
One of the biggest advantages of machine learning is, it lets us do things quickly as compared to any other way. Although it can’t solve problems which we still can’t solve but it can easily take a large volume of data and effectively establish connections and predictions on the basis of these data.
That again becomes very important as the data keeps on growing which we connected through IOT and many connecting devices. The features of machine learning will not be restricted to any particular sector but that will benefit every industry. It simply needs a better understanding of the data. Like if it’s a manufacturing plant, then it’s trying to anticipate repairs or they are planning to make a driver-less car.
Some of the industries currently enjoying the features of machine learning are Facebook Bots group, Facebook DataMining / Machine Learning /AI Group, LinkedIN machine learning and Data science group, LinkedIn Pattern Recognition, Data Mining, Machine Intelligence and Learning Group , LinkedIn Machine Learning Connection Group , Reddit/ Machine Learning, Quora machine Learning, online shopping sites like Amazon, Myntra, Flipkart and the current trend of chatbot.