Скачать с ютуб How to Build Your First KNN Python Model in scikit-learn (K Nearest Neighbors) в хорошем качестве

How to Build Your First KNN Python Model in scikit-learn (K Nearest Neighbors)

Today we are taking a look at how to build a machine learning python K Nearest Neighbors model within Jupyter. This python coding tutorial is a great way to build your first model and is quite easy. 500 Hits CSV: https://github.com/RyanNolanData/YouT... Interested in discussing a Data or AI project? Feel free to reach out via email or simply complete the contact form on my website. 📧 Email: [email protected] 🌐 Website & Blog: https://ryannolandata.com/ 🍿 WATCH NEXT Scikit-Learn and Machine Learning Playlist:    • Scikit-Learn Tutorials - Master Machi...   Decision Tree Classifier:    • How to Build Your First Decision Tree...   Logistic Regression:    • Hands-On Machine Learning: Logistic R...   Random Forest Classifier:    • Random Forest Algorithm Explained wit...   MY OTHER SOCIALS: 👨‍💻 LinkedIn:   / ryan-p-nolan   🐦 Twitter:   / ryannolan_   ⚙️ GitHub: https://github.com/RyanNolanData 🖥️ Discord:   / discord   📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan WHO AM I? As a full-time data analyst/scientist at a fintech company specializing in combating fraud within underwriting and risk, I've transitioned from my background in Electrical Engineering to pursue my true passion: data. In this dynamic field, I've discovered a profound interest in leveraging data analytics to address complex challenges in the financial sector. This YouTube channel serves as both a platform for sharing knowledge and a personal journey of continuous learning. With a commitment to growth, I aim to expand my skill set by publishing 2 to 3 new videos each week, delving into various aspects of data analytics/science and Artificial Intelligence. Join me on this exciting journey as we explore the endless possibilities of data together. *This is an affiliate program. I may receive a small portion of the final sale at no extra cost to you.