Course: Supervised learning with scikit-learn

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- Supervised Learning with Scikit-Learn 🐍
  
  Master the fundamentals of machine learning with Python's powerful Scikit-Learn library in our comprehensive course. Launched in 2021, this program includes video lessons, notes, tutorials, and quizzes to solidify your learning.
  
  Benefit from practical coding examples and curated resources from top universities, GitHub repositories, and more. Get hands-on experience and enhance your skills with real-world applications of supervised learning.
  
  Keep learning, creating, and sharing! 💻✌️
  
  🚫 Guest access is limited. To unlock full course content, please enroll in the course.
- Day 48 Quiz of Machine Learning
- 👉📚GitHub Repository URL
- 👉 📝Notebook 1 URL
- 👉 📝Notebook 2 URL
- 📚Best Free Resources to Learn Supervised Learning with Scikit-learn
  
  Welcome to an Exciting Chapter on ML with Sklearn!
  
  This chapter includes a collection of links to various courses and resources about Supervised learning with skit learn. Whether you're a beginner or an experienced learner, there's something here for everyone!
- 📚Chapter: 1- Classification ⭐️
  
  Explore implementation of supervised learning algorithm in sklearn
  
  Dive deep into Implementation of Supervise algorithm such as Logistic Regression, SVM, ANN,NB, DT,GBT etc.
- 📚Chapter:2- Regression
  
  In this chapter, learn about Introduction to regression, Stepwise Regression, LassoCV, ElasticNet, RidgeCV, Polynomial regression
- 📚Chapter:3- Fine Tuning your model
  Not available unless: You must be enrolled into this course!
- 📚Chapter:4- Data Preparation and pipelines
  Not available unless: You must be enrolled into this course!
- 📚Chapter:5- Data Exploration
  Not available unless: You must be enrolled into this course!
- 📚Chapter:6- Evaluating model performance
  Not available unless: You must be enrolled into this course!
- 📚Chapter:7- Features Selection, Importance and Feature Extraction
  Not available unless: You must be enrolled into this course!
- 📚Chapter:8-Model Explanation
  Not available unless: You must be enrolled into this course!
- ☁️Chapter:9: Model Development
  In this chapter, learn about beginner level to advance level of data exploration in Python
- Module 6. Ensemble of models
- Selecting the best model
  In this chapter, learn about Introduction of model evaluation, confusion Metrix, Accuracy, Precision-Recall-F1-score, Choose the Right Algorithm, Improved Performance of Model, Regression Metrics
  
  Bias versus variance trade-off

Supervised Learning with Scikit-Learn 🐍

📚Best Free Resources to Learn Supervised Learning with Scikit-learn

📚Chapter: 1- Classification ⭐️

📚Chapter:2- Regression

📚Chapter:3- Fine Tuning your model

📚Chapter:4- Data Preparation and pipelines

📚Chapter:5- Data Exploration

📚Chapter:6- Evaluating model performance

📚Chapter:7- Features Selection, Importance and Feature Extraction

📚Chapter:8-Model Explanation

☁️Chapter:9: Model Development

Module 6. Ensemble of models

Selecting the best model