Car Price Prediction in 1 Hr : Build an ML Model with Python

Learn how to clean data, apply encoding, and train ML models using Python — all in under 60 minutes.

What you'll learn
- How to build a complete machine learning model from scratch using Python
- How to clean, preprocess, and prepare real-world car data for training
- How to apply One Hot Encoding and Label Encoding for categorical features
- How to use Linear Regression to make predictions
- How to use Google Colab for running and sharing machine learning notebooks
- How to apply core machine learning concepts to real-world problems

Requirements
- Basic understanding of Python syntax (variables, functions, loops)
- No prior experience with machine learning required
- A web browser and internet connection (we’ll use Google Colab — no installations needed)
- Curiosity and willingness to learn by doing

Description
Course Description:

Learn machine learning by building a real-world project — from start to finish — in just one hour.

This course offers a fast, focused, and practical introduction to machine learning using one of the most relatable examples: predicting car prices. You’ll work with real-world data and use industry-standard tools likePython,Pandas,Scikit-learn, andGoogle Colabto develop a complete machine learning pipeline. Best of all, there's no need to install anything — all work is done in the cloud.

This hands-on course is designed for:

Beginners who want to learn ML through practical application rather than theory

Developers curious about applying ML to real-world problems

Students looking to add a portfolio project

Anyone interested in exploring how machine learning models are trained and evaluated

Throughout the course, you’ll follow a structured, step-by-step process to build your car price prediction model. You’ll start with raw CSV data and end with a fully trained and tested ML model that can make predictions on unseen data.

You’ll learn how to:

Import and inspect real-world car pricing data

Clean and preprocess data using Pandas

ApplyOne Hot EncodingandLabel Encodingto categorical variables

Train aLinear Regressionmodel and evaluate its performance

Improve accuracy with aRandom Forest Regressor

Usetrain_test_splitto validate your model’s performance

Calculate error metrics like Mean Squared Error (MSE)

UseGoogle Colabto write, run, and share your code

By the end of the course, you will:

Understand the end-to-end machine learning workflow

Be comfortable using key tools in the Python ML ecosystem

Be able to apply what you've learned to your own datasets and problems

Have a completed, portfolio-ready machine learning project

This course is short by design — perfect for busy learners or those just getting started with ML. It emphasizes action over theory, with clear explanations and practical takeaways at every step.

Join now and take your first step into the world of machine learning — no fluff, no filler, just real results in under an hour.

Who this course is for:
- Beginners who want a hands-on introduction to machine learning
- Python developers curious about applying ML to real-world data
- Data science students looking to build a quick portfolio project
- Anyone interested in learning how to train and evaluate ML models
- Busy professionals who want to build a real ML model in under an hour
More Info