25. ML Classification Series Recap – Key Takeaways & Next Steps! 🚀 скачать в хорошем качестве

25. ML Classification Series Recap – Key Takeaways & Next Steps! 🚀

Congratulations on completing the Machine Learning Classification series! In this final video, we’ll recap everything we’ve learned, reinforce key concepts, and discuss the next steps in your ML journey. What You’ll Learn: ✅ Overview of the ML workflow – from EDA to model training ✅ Key concepts like data splitting, model evaluation, ROC curves, confusion matrix, and feature importance ✅ Hyperparameter tuning techniques: RandomizedSearchCV & GridSearchCV ✅ Running multiple models using functions & parallel processing ✅ Best practices for building and deploying ML models By the end of this video, you’ll have a solid understanding of ML classification, and I’ll share resources to help you continue your learning! 📌 Useful Links 📊 Dataset Links: 📌 Heart Disease Dataset 1: https://www.kaggle.com/datasets/johns... 📌 Heart Disease Dataset 2: https://www.kaggle.com/datasets/deeks... 🗺️ Machine Learning Workflow Map: 📌 Scikit-Learn ML Map: https://scikit-learn.org/stable/machi... 👉 Stay Tuned: Subscribe for more hands-on ML tutorials, and hit the 🔔 notification bell to stay updated! Looking for 1-on-1 Training? I offer personalized training sessions on Machine Learning, BigQuery SQL, and Google Sheets Apps Script! Whether you're a beginner or looking to refine advanced skills, I’ve got you covered. 📩 Contact me at: [email protected] 🔖 Hashtags for Better Reach: #MachineLearning #MLRecap #DataScience #MLforBeginners #LearnMachineLearning #ScikitLearn #PythonProgramming #AI #DataAnalytics #ModelEvaluation #CodingTutorial