Scikit-Learn for Beginners: Build Your First ML Model

Scikit-learn (Sklearn) is one of the most powerful and beginner-friendly machine learning libraries in Python. In this tutorial, Dr. Uohna Thiessen, AI strategist and Udacity instructor, walks viewers through the process of building their first machine learning model using Scikit-learn.

This video covers the basics of machine learning, from loading and exploring data to training and evaluating a model. Using the famous Iris dataset, Dr. Thiessen demonstrates how to set up a machine learning workflow in Google Colab with just a few lines of Python code.

What This Video Covers:
- An introduction to Scikit-learn and its capabilities
- The five key steps in machine learning model development
- How to load, explore, and split data for training
- A hands-on walkthrough of building a simple prediction model
- How to evaluate the model’s performance

By following along, viewers will gain a fundamental understanding of machine learning and how to apply Scikit-learn to real-world problems, from customer behavior prediction to recommendation systems.

For those looking to dive deeper into machine learning, check out the official Scikit-learn documentation: https://scikit-learn.org/

If you find this tutorial helpful, consider subscribing for more machine learning and AI tutorials. Let us know in the comments what topics you’d like to see next.

---

Follow Dr. Uohna Thiessen on LinkedIn: https://www.linkedin.com/in/druohna-datascientist/

Continue learning Machine Learning at Udacity: https://www.udacity.com/school/artificial-intelligence

---

Connect with us on social! 🌐
Instagram: https://www.instagram.com/udacity/
LinkedIn: https://www.linkedin.com/school/udacity/
Facebook: https://www.facebook.com/Udacity/
X/Twitter: https://twitter.com/udacity


---

Video Chapters:
00:00 - Introduction to Machine Learning and Scikit-learn
00:45 - What is Machine Learning?
02:00 - Understanding the Machine Learning Workflow
03:00 - Introduction to Google Colab and Python Libraries
04:00 - Loading and Exploring the Iris Dataset
06:30 - Data Splitting: Features and Targets
07:30 - Training a Machine Learning Model with Scikit-learn
09:00 - Evaluating Model Performance
10:20 - Conclusion and Next Steps