What are the Scopes of Machine Learning in the Future?

An Introduction to Machine Learning / What is Machine Learning?

Machine learning can equip a system with mere common sense as that of a human being. That is, to behave automatically, learn or improve according to previous experience without explicit programming. Machine Learning is an application of artificial intelligence which is the technology that replicates human behaviors conventionally regarded as ‘intelligent’ in machines. Generally, the tasks performed out of Artificial Intelligence include image recognition, language translation, information retrieval, reasoning based on logic or evidence, planning and navigation. They broadly fall into three groups: sensing, reasoning and communicating.

However, this process learning begins with a keen observation of data searching for a pattern and helping out to take better decisions in the future. The systems equip themselves to adjust actions accordingly without any human assistance.

What are the Scopes of Machine Learning in the Future?1

Some Machine Learning Methods

Let us talk about some commonly used machine learning methods.

1. Supervised Machine Learning Algorithms

Analyzing and interpreting data patterns with the help of input and function. The system uses labeled examples to apply what is learned from the past to new data in order to predict future events. For example, you can use an algorithm to understand the mapping function from input to output.

Y=f(X).

Here x is the input variable and Y is the output variable. We aim to predict the output variables Y for a new input data x. The algorithm is basically understood from a training dataset, as supervised by a teacher. The learning algorithm compares its output with the correct, intended output and finds errors so as to modify the model accordingly. The learning process comes to an end when the algorithm attains an acceptable grade for performance.

2. Unsupervised Machine Learning Algorithms

Unsupervised Machine Learning has only input variables x and no corresponding output variables. This is critical when the training data set is neither classified nor labeled. Though the system cannot figure out an exact expected output, it honestly attempts to find out an underlying structure of the unlabeled data. Algorithms are set free to their own devices to search out and present the interesting structure in the training data set. There is no right outcomes or a teacher.

3. Semi-supervised Machine Learning Algorithms

As the name shows, this remains in between supervised and unsupervised Machine Learning Algorithms. These problems have a large amount of Input data and only a few of the data is labeled Y. This is an emerging scenario as labeling is expensive and requires access to domain experts and unlabeled data is cost-efficient, easy to collect, and store. Here a mixture of supervised and unsupervised techniques are used.

Merging of machine learning and Artificial Intelligence along with cognitive technologies makes it even more effective to process large volumes of information.

Machine Learning Examples

To gain a deeper understanding of the concept, we shall discuss some examples of machine learning which we come across in our day to day lives.

Social Media Services

All of us are really active in social media these days. I bet nobody lacks a Facebook account. But have you ever thought of the machine learning skills presented there? Some of them include,

1. “People you may know”

Facebook keenly observes your profile, where you worked, where you have been worked, your educational institutions, the profiles you visit. On the basis of continuous learning, Facebook come up with a list of possibilities headed “people you may know”.

2. Face Recognition

When you upload a picture with your close friend, Facebook instantly recognizes your friend after comparing it with your friends’ list. The process is really complicated, but seems to an excellent example of machine learning from the front end.

3. Online Customer Support

Nowadays, most of the websites are equipped with 24×7 online customer support. They are artificially intelligent chatbots that extract information from the website with respect to the query and present to the customer. Machine Learning Algorithms transform chat bots more intelligent day by day.

4. Product Recommendations

Product recommendations are another example of machine learning which all you have certainly come across at least once in your life. It keenly observes your behavior with the website, what you have shopped before, what you have added to your cart, etc., machine learning performs the magic of product recommendations for you.

5. Improving Search Results

Search Engines detect the search results you open and act accordingly to refine the search engine results. If you open the top results every time and stay on those pages for a long time, the search engine understands the displayed results matched your query. If you do not open any of the results, it concludes the results did not match the query of the user.

Examples

Some other examples of machine learning include image recognition, speech recognition, medical diagnosis, etc.

 

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Applications of Machine Learning

All the above-stated examples can be understood as the applications of machine learning in various sectors. Let us talk about some upturned aspects of the machine learning application.

1. Machine learning in the financial sector

Machine Learning can help companies that deal with finance to detect financial frauds and to help them by suggesting the best possible investment options and a lot more. It is always good for financial companies to go hand in hand with machine learning companies to stay away from finance related issues.

2. Application of Machine Learning in the health sector

The sensors provide some basic health information like the heartbeat, blood pressure, etc. to help the doctors easily diagnose the health condition of the patient. Sensors are fixed in the clothing of the patient and the data collected helps the doctors to predict the health condition of the patient.

3. Application of machine learning in traveling

Everyone travels with the help of GPS these days. Machine learning can be used to predict upcoming traffic on your way when connected to the central server for traffic management. Machine learning can predict the estimated cost of a ride while you book a cab, it reduces the price when you opt to share a ride, and a lot more!

4. In Some other applications

Some other applications include detection of crime before it happens in video surveillance, E-mail spam, and malware filtering, Accelerating sales and marketing, applications in virtual personal assistants, etc.

Machine learning consistently makes our lives easier and entertaining. This is really how technology benefits our daily lives. Stay close to machine learning and enjoy its benefits in the various domains of your daily life.

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