Basics of Machine Learning Machine learning involves training machines to learn from

Basics of Machine Learning

Machine learning involves training machines to learn from past data, understand and reason. It is more than just learning as it involves prediction and classification of new data. In this article, we will explore the basics of machine learning.

Example:

Let’s consider an example of Paul who likes songs with fast tempo and soaring intensity while he dislikes songs with relaxed tempo and light intensity. If we plot the songs on a graph with tempo on the x-axis and intensity on the y-axis, we can easily classify the unknown song based on Paul’s past choices. However, if the choice becomes complicated, as in the case of a song with medium tempo and intensity, machine learning comes in to learn from past data, build prediction models and predict new data points.

Supervised Learning:

Suppose your friend gives you a dataset of one million coins of three different currencies, and each coin has different weights. When you feed this data to the machine learning model, it learns which feature is associated with which label. Hence, supervised learning uses labeled data to train the model.

Unsupervised Learning:

Suppose you have a cricket dataset of various players with their respective scores and wickets taken. When you feed this dataset to the machine, it identifies the pattern of player performance and clusters them as batsmen and bowlers. Here, there were no labels of batsmen and bowlers, hence the learning with unlabeled data is unsupervised learning.

Reinforcement Learning:

Reinforcement learning works on the principle of feedback. Suppose you provide the system with an image of a dog and ask it to identify it. The system identifies it as a cat. You give a negative feedback to the machine, and it learns from the feedback and classifies correctly in the future.

Applications of Machine Learning:

Machine learning is used in healthcare, sentiment analysis, fraud detection in finance, predicting customer churn in eCommerce, surge pricing in the transportation sector, and many more.