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? 👇 #MachineLearning #Python #pythonprogramming #ml #ai #DataScience #artificialintelligence #pythontutorial #ScikitLearn #coding #codingforbeginners