Machine Learning Basics – Regression and Classification Models #AIMLSeries-1

I used to be fascinated when someone said, “I’m into Machine Learning and building a model.” I always imagined it had to be something super complex — like a deep neural network.

But now I’ve come to realize that it often starts with something much simpler, like a basic regression or classificationmodel.

So let’s begin exploring Machine Learning — one step at a time.

What is Machine Learning?

Machine Learning is about training computers to learn from past data so they can find patterns and make predictions. Most ML problems fall into two types:

1. Regression – Predicting a number

2. Classification – Predicting a category or label

1- Regression Models

This is used when the goal is to predict a continuous value. We can use this to predict how a dependent variable(say Y) changes based on changes in the independent variable (say X) in a simple equation like Y=mX+c.

Example: Predicting house prices using features like size, location, and number of rooms.

Common uses:

• Sales forecasting
• Salary estimation
• Weather prediction
• Health metrics like blood pressure

Popular Regression Models:

• Simple Linear Regression – Predicts using a straight line and one input variable.
• Multiple Linear Regression – Predicts using a straight line with multiple input variables.
• Polynomial Regression – Fits a curve to capture nonlinear patterns.
• Decision Tree Regression – Makes predictions by splitting data into decision paths.
• Random Forest Regression – Averages results from multiple decision trees for better accuracy.

2- Classification Models

This is used when the goal is to sort data into categories. We can use this to predict a yes/no or true/false outcome. Example: Predicting whether an email is spam or not.

Common uses:

• Medical diagnosis
• Loan approval
• Image recognition
• Sentiment analysis

Popular classification models:

• Logistic Regression – for binary outcomes
• Decision Trees – rule-based and easy to understand
• Random Forest – combines multiple trees for better results
• Neural Networks– handles complex patterns in images and text

Key Differences

Basic Steps in Any ML Project

• Collect data
• Clean and do Exploratory Data Analysis (EDA)
• Choose a Model
• Train it
• Test and Evaluate
• Improve if needed
• Deploy in real-world use

More learnings soon where we will cover some of these topics in details !