Simple Machine Learning Code Tutorial for Beginners with Sklearn Scikit-Learn
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:
• Faster Scikit-learn on GPU with NVIDIA cuM...
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:
• Anaconda Beginners Guide for Linux and Win...
2. What's "features", "samples", and "targets"? Detailed explanation with real-life examples:
⭐ Machine Learning FOR BEGINNERS - Supervised, Unsupervised and Reinforcement Learning:
• Machine Learning FOR BEGINNERS - Supervise...
3. What's Linear Regression?
⭐ Linear Regression Algorithm with Code Examples:
• Linear Regression Algorithm with Code Exam...
📌 CODE RESOURCES 📌
Download my code: https://github.com/MariyaSha/scikit_l...
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? 👇
#MachineLearning #Python #pythonprogramming #ml #ai #DataScience #artificialintelligence #pythontutorial #ScikitLearn #coding #codingforbeginners
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