An Introduction to Machine Learning and How It Works

Does the mention of machine learning bring up an image of robots in the distant future in your head? But that technology has become a reality now. Machine learning powers algorithms in the way that they solve difficult problems that improve our daily lives.

We know how are navigation apps rely on GPS satellites so that they can show us the location and the algorithms of machine learning will help us figure out the easiest route. But when you add a complex issue to this, like a traffic jam. What are the solutions to that problem? Machine learning can be of immense help here.

What is Machine Learning?

Traditional algorithms take help of human app developers in order to receive clear instructions. Machine learning allows the data to facilitate this process. The computer creates a model with a training set of representative data which can be put against an expected output. The model will be better and more accurate depending on the data the computer can take into consideration. This all can take place without the need for developers having to write additional lines of code.

How Does It Work?

The rise of big data was facilitated by improvements in storage and processing. These same advancements have also helped machine learning in improving. The storage and processing frameworks have helped the machine learning algorithm in storing and processing an immensely huge amount of unstructured data. This is of great help to data scientists, as the potential to create algorithms that are conducted by actual data is a big advantage.

When it comes to a navigation app, machine learning will help the app in improving its recommendation algorithms that are established from historical data from previous trips that you have taken. With the help of this data, the algorithm can predict a solution for a route that might be crowded or congested at a particular time of the day. Based on different situations, different routes will be suggested. The developer will never have to tell the user that there is rush hour traffic, the computer will simply conclude this from the data available, and modify the model accordingly.

What if there is a major change in the underlying source of data?

Suppose there is an alternate road or flyover built which changes the way people get around that locality completely. In such cases, this model will not be accurate. This is the reason why it is extremely important to retrain your algorithm from time to time. It should be added with more data set that are up-to-date.

Machine learning creates algorithms based on real-world datasets. Problems that were previously considered unsolved can now be solved easily. Capturing the complexities of a human language in an algorithm was a big challenge. But it changed with machine learning. There were training sets created by linguists to tag parts of speech. With the help of those training sets, models were created that could conclude the syntax of the language and the rules of grammar. Along with this, a tolerance for grammatical and spelling errors was also given.

The machine learning techniques can be applied to different industries.

1. Anomaly detection. This is the process of screening through piles of data and learning to figure out the instances that diverge from recognized patterns. This detection technique is largely used by email programs like Outlook and Gmail.
2. Natural Language Processing. This process allows the computer to make sense of a human language, be it in writing or speech.
3. Recommendation engines. Here, the data about user’s behavior is gathered and analyzed in order to predict what the users might like based on how similar the users are.
4. Named Entity Recognition. This is the capability to identify entities like titles and addresses in a document. Phone numbers and dates can be automatically identified in Apple’s Messages app.
5. Image Recognition. This function allows a computer to cite or extricate meaning from still images and videos.

Wrap up

This is still the very beginning of machine learning and the actual potential has not been recognized yet. But we can expect that to happen soon. You can integrate machine learning into your business and see how it changes the way your business works. Want More? To learn more about machine learning or get professional help, get in touch with Nimblechapps. Stay tuned for more articles on Machine Learning.

Keval Padia
Keval Padia is the founder & CEO of Nimblechapps, a fast-growing mobile game development company. The current innovation and updates of the field lures him to express his views and thoughts on certain topics.