If we talk about supervised or unsupervised learning, the training set will usually be in a table form.  

Consider the **diabetes dataset**, which has the task of predicting whether a person has diabetes. It holds information about 768 females with parameters like age, body mass index, blood pressure, etc. These parameters are called **features**. 
 
The dataset also contains information on whether the person has diabetes in an `'Outcome'` column, which is what we want to predict. It is called **target**.  

Each row in a table is called **instance**(or **data point** or **sample**). In this case, it is information about one female.

The table (training set) has a **target column** in it, which means it is labeled.  

The task is to train the ML model on this training set, and once it is trained, it can predict for other people (**new instances**) whether they have diabetes based on features only.

While coding, feature columns are usually assigned to `X` and target columns assigned as `y`.

And features of new instances are assigned as `X_new`.

Machine learning is now used everywhere. Want to learn it yourself? This course is an introduction to the world of Machine learning for you to learn basic concepts, work with Scikit-learn – the most popular library for ML and build your first Machine Learning project.
This course is intended for students with a basic knowledge of Python, Pandas, and Numpy.

Learn the Machine Learning concepts and the ML project workflow.

Preprocessing is probably the most important stage of an ML project. This chapter covers the preprocessing steps needed for almost any dataset.

A pipeline is a neat way to combine all the preprocessing steps as well as a model. Pipelines make it much easier to train and use a model.

Modeling is the most fun stage of an ML project. Let's learn to build, fine-tune and evaluate the model!

ML Introduction with scikit-learn

Training Set

Match the variable names with the data they usually hold.