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:
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:
2. What’s “features”, “samples”, and “targets”? Detailed explanation with real-life examples:
⭐ Machine Learning FOR BEGINNERS – Supervised, Unsupervised and Reinforcement Learning:
3. What’s Linear Regression?
⭐ Linear Regression Algorithm with Code Examples:
📌 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
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