Machine Learning falls under the umbrella of Artificial Intelligence, with a primary focus on enabling computer systems to learn from available data and make decisions or predictions without explicit programming. It encompasses three distinct types of Machine Learning: Supervised learning, Unsupervised learning, and Reinforcement learning.

Supervised learning

Supervised learning uses labeled training data to teach models to yield the desired output. This training dataset includes inputs and correct outputs, which allow the model to learn over time. (**IBM Cloud Education, 2020**). Read more here

Supervised learning tasks

• Classification: The use of machine learning algorithms to assign data into categories i.e spam or ham mail, violent or non-violent tweets, plant species classification e.t.c.
• Regression: The use of machine learning algorithms to make predictions of continuous numeric values such as house prices, flight ticket prices, stock prices prediction, product sales prediction e.t.c.

Unsupervised learning

Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention or correct output. (**IBM Cloud Education, 2020**).

Read more on unsupervised learning here.

Try to grasp the essence of what has been discussed today, as it forms the foundation for everything you will be undertaking. These concepts are crucial for every Data Scientist to internalize and understand.