What is Machine Learning and It’s Applications

Machine learning (ML) is a branch of computer science. That focuses on utilizing data and algorithms to learn and perform tasks similar to human intelligence. There are two main types of learning techniques available in machine learning i.e. supervised learning and unsupervised learning.

• Supervised Learning: In this learning method, the predefined samples or examples are used to perform learning of the algorithm. The example patterns include the feedback to adjust and improve learning. Some of the popular supervised learning algorithms are Support Vector Machine (SVM) and Artificial Neural Network.
• Unsupervised Learning: In this learning method, algorithms are applied directly to learning data and use a heuristic function to enhance the learning accuracy. These techniques are used for clustering, categorization, and segmentation. Some popular unsupervised learning algorithms are Fuzzy C Means Clustering (FCM) and K-Means Clustering.

Using Machine Learning, we can perform decision-making, forecasting, optimization, pattern recognition, and many more things. This technology is also helpful to understand the data. Therefore, machine learning technology is a set of algorithms and processes to analyze and identify meaningful insights. That is useful for application development. By using ML techniques, many applications have become feasible, some essential of them are given below:

• Recommender Systems: It is a popular ML application widely used in e-commerce platforms to assist consumers in selecting the appropriate products and services online.
• Fraud Detection: ML technology is also assisting banking and finance companies in analyzing loan and credit card applications to find fraud. Additionally, it can also be used for monitoring banking transactions and activities.
• Stock Market Trading: Nowadays, a number of applications developed that automatically analyze the stock market data and predict the future possible consequences to manage the risk of financial loss.
• Medical Science: ML techniques are frequently used to analyze medical health records to predict disease risk and prevent critical human losses.