Beginner's Guide to Machine Learning with Scikit-Learn: Easy Step-by-Step Tutorial 🤖

Ready to dive into practical Machine Learning using the easiest library in the world?? 🚀🚀🚀
Allow me to introduce you to this fascinating field of science through a step by step Scikit-Learn example!

🛑 ANNOUNCEMENT 🛑
Scikit Learn is now running up to x50 FASTER on GPU! Check out my follow up tutorial:
⭐ Faster Scikit-Learn with NVIDIA cuML:
https://youtu.be/mxtSO0EGgtw

Scikit-Learn, or Sklearn, is a popular open source library designed for simple, impactful, and human-readable workflows. In this beginner-friendly tutorial, I will walk you through a complete machine learning project to build, train, test, and optimize an AI model with Python’s Scikit-Learn!
This video is perfect for those who are new to data science, or those who have a basic background but need to polish their practical skills. 💪

Best part is - this tutorial breaks down complex concepts like Polynomial Features, Hyperparameter Tuning, and Model Evaluation into simple, logical and easy-to-understand steps!! In addition, I'll provide you with further learning resources that will help you grasp all the rest 🐍💻💡

🤓 WHAT YOU'LL LEARN 🤓
- Installing Scikit-Learn and setting up your environment.
- Loading and exploring built-in datasets (California Housing Data).
- Splitting data into training and testing sets.
- Training models with different algorithms (Linear Regression, Random Forest, and Gradient Boosting).
- Optimizing models with Polynomial Features and Hyperparameter Tuning.
- Evaluating models with R² scores.
- Saving and loading models with Joblib.

💡 WHY WATCH? 💡
This tutorial is designed for beginners with minimal coding and ML experience. I use clear, jargon-free explanations and practical examples to help you confidently start your machine learning journey. By the end, you’ll have a solid workflow to tackle your own ML projects! 🌟

🛑 PLEASE NOTE 🛑
AveOccup inside the California Housing dataset, represents the average n umber of occupants per household instead of the "profession" of the residents. My apologies for not spotting it earlier! 🙏

⏰ TIME STAMPS ⏰
00:53 - install sklearn
02:00 - load dataset from sklearn
04:43 - train test data split
06:07 - random state
07:25 - training with sklearn
08:36 - predict with sklearn for testing and evaluation
09:44 - r2 metric for evaluation
11:06 - baseline model
11:34 - polynomial features
14:11 - algorithm optimization
16:34 - n jobs faster processing
17:55 - hyperparameter tuning
21:10 - save and load sklearn model

📚 FURTHER LEARNING 📚
If at any point in this video you find yourself stuck or wondering "what on Earth is she talking about??", please check out some of my previous tutorials below for detailed explanations:

1. What's Anaconda?
⭐ Anaconda Beginners Guide for Linux and Windows:
https://youtu.be/MUZtVEDKXsk
2. What's "features", "samples", and "targets"? Detailed explanation with real-life examples:
⭐ Machine Learning FOR BEGINNERS - Supervised, Unsupervised and Reinforcement Learning:
https://youtu.be/mMc_PIemSnU
3. What's Linear Regression?
⭐ Linear Regression Algorithm with Code Examples:
https://youtu.be/MkLBNUMc26Y

📌 CODE RESOURCES 📌
- Download my code: https://github.com/MariyaSha/scikit_learn_simplified
- Scikit-Learn Documentation: https://scikit-learn.org/

🔔 Don’t forget to LIKE, SUBSCRIBE, and hit the bell for more Python tutorials! 👍
💌 Share your thoughts in the comments—what ML project will you build next? 👇

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