Machine Learning helps software applications become accurate at making predictions without having to be explicitly programmed for the same. Machine learning algorithms are able to predict new output values by using historical data.
A common use case for this field is the recommendation engines. Other applications include spam filtering, fraud detection, predictive maintenance, business process automation (BPA), and malware threat detection.
Types of Machine Learning
Machine learning is categorized by the strategy used by the algorithm for increasing its accuracy while making predictions. There are four approaches that are selected on the basis of the type of data that must be predicted:
- Supervised learning – This type of machine learning involves supplying labeled training data to the algorithms and defining the variables that must be assessed by the algorithm for correlations. The algorithm’s input and output are specified.
- Unsupervised learning – In this machine learning, the algorithm is trained using unlabeled data. The algorithm will scan the dataset for a meaningful correlation. The data used for training and the recommendations or predictions made by the algorithm are predetermined.
- Semi-supervised learning – This machine learning approach involves mixing the above two types. An algorithm is fed mostly labeled training data. However, the model can explore the data and develop its own understanding.
- Reinforcement learning – This type is used for teaching a machine to finish a multi-step process with clearly-defined rules. A data scientist will program an algorithm and give it negative or positive ques for completing a task. But, the algorithm will decide what steps it will take along the way.
Uses of Machine Learning
Machine learning has found a wide range of applications. The most popular application is the recommendations system used by top companies like Facebook. With Machine Learning, Facebook is able to personalize every user’s feed.
If a member reads the posts of a particular group, the recommendation system will start showing that group’s activity more in the feed.
This system reinforces known patterns in the online behaviour of the user. If the member changes patterns and doesn’t read that group’s posts, the feed will be accordingly adjusted.
Apart from the recommendation system, here are a few other applications of machine learning:
- Customer Relationship Management – Machine learning models are used in CRM software for analyzing emails and giving priority to the important messages first. In fact, an advanced system can recommend an effective response.
- Business Intelligence – Analytics and BI vendors can use machine learning for identifying important data points, anomalies, and patterns of data points.
- Human Resource Information Systems – These systems use machine learning models for filtering through applications and identifying the best candidate for a job.
- Self-driving cars – By using machine learning algorithms, self-autonomous cars are able to recognize an object and alert the driver.
- Virtual assistants – These assistants use a combination of supervised and unsupervised learning models for interpreting natural speech and supplying context.
Selecting the Right Machine Learning Model
If the right strategy is not used, the task of selecting the right ML model for solving a problem can become time-consuming. Here are a few steps that must be followed:
- The problem must be aligned with the potential data inputs that are considered for the solution. In order to do so, data science professionals must have an in-depth understanding of the problem.
- Next, the data must be collected, formatted, and labeled, if necessary. For this step, data scientists often have to work with data wranglers.
- Select the algorithm that will be used and test them to see how well they are performing.
- Fine-tune the outputs until they get an acceptable accuracy level. Data scientists perform this task while taking feedback from experts with a thorough understanding of the problem.
If the machine learning model is complex, explaining how it works can be difficult. In some vertical industries, data scientists use simple models as it is important to explain how a decision was made.
Examples of such industries are the ones with heavy compliance burdens such as insurance and banking. And even though complex models provide accurate predictions, explaining them and the output to a layperson can be challenging.
The Future of Machine Learning
It’s been decades since the advent of machine learning. As Artificial Intelligence (AI) grows in prominence, machine learning continues to gain new popularity as well. Deep learning models are used for powering some of the most advanced AI applications.
ML platforms are among the most competitive realms today with major vendors like Amazon, IBM, Microsoft, Google, and others.
These companies are in a race to get customers for their platform services that cover the entire spectrum of machine learning such as data collection, preparation, classification, model building and training, and application deployment.
Now that the importance of machine learning in business operations has increased, AI has become more practical. In the coming years, the war between machine learning platforms will intensify.
Researchers all over the world are looking into deep learning and AI to develop general applications. They are working on techniques through which the machines will be able to apply the context they learned from one task to a different task in the future.
By gathering customer data and making a correlation with behaviors, machine learning algorithms are able to learn associations and tailor marketing initiatives and product development as per the demands of the customers.
In fact, some internet companies implement machine learning in their business models as their primary driver.
An example of this is Uber that matches drivers with riders using algorithms. Google uses machine learning for providing the right advertisement in its searches.
With so many companies using machine learning, now is the time to get started in the field. Anyone interested in working as a Machine Learning Engineer must enroll in AI ML courses to get an edge over their counterparts.