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AlgoStalk
เข้าร่วมเมื่อ 29 ส.ค. 2024
🔍 Algo Stalk: Your one-stop journey to stalk algorithms from basics to advanced AI!
✨ Explore: Machine Learning, AI concepts, and their magic.
🧠 Learn: The math behind algorithms, simplified for everyone.
💻 Code: Hands-on Python implementation for real-world applications.
📚 Format:
1️⃣ Concept breakdown
2️⃣ Math deep dive
3️⃣ Python code
4️⃣ Combined videos & real-life examples 🌍
Let's stalk those algorithms together! 👀 #AI #MachineLearning #Python #DataScience
Subscribe to stay ahead in the world of algorithms! 👀 #MachineLearning #AI #Python
✨ Explore: Machine Learning, AI concepts, and their magic.
🧠 Learn: The math behind algorithms, simplified for everyone.
💻 Code: Hands-on Python implementation for real-world applications.
📚 Format:
1️⃣ Concept breakdown
2️⃣ Math deep dive
3️⃣ Python code
4️⃣ Combined videos & real-life examples 🌍
Let's stalk those algorithms together! 👀 #AI #MachineLearning #Python #DataScience
Subscribe to stay ahead in the world of algorithms! 👀 #MachineLearning #AI #Python
Meet the Simple yet Effective Detectives: The Cousins of Random Forest (Bagging Family)
Join us on an exciting detective-themed journey as we uncover the hidden talents of Random Forest's lesser-known yet powerful cousins! Meet the Bagging family-Bagging, Pasting, Random Subspace, and Random Patches-and see how each detective tackles the mystery of 'The Stolen Pattern.' This detailed video covers concepts. With dummy data, learn when and why to choose each algorithm for your data science challenges. Don’t miss out on the action, drama, and learning!
Random Forest Regressor: th-cam.com/video/WqKgrz0-NxI/w-d-xo.html
#bagging #randomforest #machinelearning #datascience
Random Forest Regressor: th-cam.com/video/WqKgrz0-NxI/w-d-xo.html
#bagging #randomforest #machinelearning #datascience
มุมมอง: 12
วีดีโอ
XGBoost Quantile Regression: Unlock Data Insights
มุมมอง 1821 วันที่ผ่านมา
Dive into the world of XGBoost Quantile Regression! In this tutorial, we’ll explore how quantiles like 2.5%, 50%, and 97.5% can help you move beyond averages to understand the full range of your data. Learn the concepts, mathematical foundations, and practical applications of quantile regression in XGBoost. Key Topics Covered: - What are quantiles and why they matter? - The difference between m...
XGBoost EXPLAINED: Stop Copy-Pasting and Finally Understand It!
มุมมอง 45หลายเดือนก่อน
Do you feel like you're just copying and pasting XGBoost code without truly understanding how it works? Don’t worry-you’re not alone! This video dives deep into the concepts of XGBoost, the most powerful and widely-used gradient boosting algorithm in machine learning. And here’s the best part-no coding is required to grasp it all. In this video, we’ll cover: 🔍 What makes XGBoost faster and smar...
Inside AdaBoost Regressor: How It Learns and Adapts
มุมมอง 32หลายเดือนก่อน
Struggling to improve your regression models and tackle complex predictions? 🤔 Curious how AdaBoost Regressor combines weak learners to create a powerful predictive model? 🕵️♂️ In this video, we break down AdaBoost Regressor step by step, showing you how it updates weights, focuses on hard-to-predict samples, and builds a strong ensemble model. Whether you’re just starting out or refining your...
Gradient Boost Regressor: Tackling Complex Prediction Errors with Precision#boosting #gradientboost
มุมมอง 24หลายเดือนก่อน
Curious about boosting your predictive models to the next level? 🚀 Enter Gradient Boost Regressor! In this video, we dive into the mechanics of Gradient Boosting, a powerful technique that builds multiple weak models in sequence, each correcting the errors of the last. 🌟 But what makes Gradient Boosting so effective-and why has it become a go-to choice for many machine learning practitioners? J...
Extra Tree Regressor vs. Random Forest: Fast, Randomised in ML Explained #machinelearning
มุมมอง 78หลายเดือนก่อน
Curious about the power of Extra Tree Regressors in machine learning? 🌳 Ever wonder how they're different from Random Forests or why they’re faster? 🕵️♂️ In this video, we uncover the secrets of the Extra Tree Regressor, explaining how it uses added randomness to build efficient, robust models. Perfect for those who want to enhance prediction speed without losing accuracy! Whether you're a dat...
Random Forests Regression! 🌲💪 Understand Using Mid-School Level Maths
มุมมอง 242 หลายเดือนก่อน
Description: Ready to elevate your predictive accuracy to the next level? In this video, we explore the Random Forest Regressor-a powerful ensemble technique that combines multiple Decision Trees to make better predictions. 🌲🔍 Whether you're dealing with complex datasets or trying to identify patterns in noisy data, Random Forest can bring clarity to your data investigations. Let’s dive into ho...
Master Decision Tree Regression! Using Elementory Maths🌳🔍
มุมมอง 402 หลายเดือนก่อน
Curious how a single model can predict complex outcomes with ease? In this video, we dive deep into the world of Decision Tree Regressors-a powerful, intuitive tool for uncovering hidden patterns in your data! Whether you're trying to predict sales based on weather or forecast future trends using different factors, Decision Trees can guide you step-by-step through the clues to reveal the answer...
Outliers in Data? RANSAC Regressor or Theil-Sen Regressor - Which Model Wins?
มุมมอง 402 หลายเดือนก่อน
Outliers in Data? RANSAC Regressor or Theil-Sen Regressor - Which Model Wins?
Can your Regression Model Handle Outliers? | HuberRegressor
มุมมอง 402 หลายเดือนก่อน
Can your Regression Model Handle Outliers? | HuberRegressor
SGD Regressor! Things You MUST Know #datascience #sgd
มุมมอง 862 หลายเดือนก่อน
SGD Regressor! Things You MUST Know #datascience #sgd
Master Regularization: Linear vs Ridge vs Lasso vs Elastic-Net | #regularization #machinelearning
มุมมอง 1443 หลายเดือนก่อน
Master Regularization: Linear vs Ridge vs Lasso vs Elastic-Net | #regularization #machinelearning
Linear vs Ridge vs Lasso vs Elastic-Net | #regularization #machinelearning
มุมมอง 303 หลายเดือนก่อน
Linear vs Ridge vs Lasso vs Elastic-Net | #regularization #machinelearning
How Ridge, Lasso, and Elastic Net Boost Your Linear Regression Models
มุมมอง 223 หลายเดือนก่อน
How Ridge, Lasso, and Elastic Net Boost Your Linear Regression Models
Learn Linear Regression in Python (Part 3) #linearregression
มุมมอง 823 หลายเดือนก่อน
Learn Linear Regression in Python (Part 3) #linearregression
Linear Regression Maths (Part 2) #linearregression #machinelearningmath
มุมมอง 563 หลายเดือนก่อน
Linear Regression Maths (Part 2) #linearregression #machinelearningmath
Linear Regression Concept (Part 1) #linearregression #regressionmodel
มุมมอง 843 หลายเดือนก่อน
Linear Regression Concept (Part 1) #linearregression #regressionmodel
Amazing, this video really helped me a lot. Thank you.
Glad to hear that!
that is a great explanation i ever seen dont give up
@@music_vevo thank you for your comment. It boosts huge confidence.
Great video, thank you for explaining this. And I think we can use quantiles in as simple as RF models?
Yes we can but it is not directly available in most popular library like sklearn. In order to use it effectively, you might wanna check skgarden library and import RandomForestQuantileRegressor.
🚀 Want to tackle outliers without losing performance? 🚀 In this video, we explore the power of Huber Regressor, a robust regression technique that combines the best of OLS and Huber loss to handle outliers while maintaining model accuracy. Have you used Huber Regressor in your projects? Drop your questions or thoughts in the comments below! 🔍👇
🚀 Ready to level up your regression skills? 🚀 In this video, we dive deep into the power of SGD Regressor and how Stochastic Gradient Descent can make your linear models more efficient. Let me know in the comments if you’ve used SGD before, and feel free to ask any questions about fine-tuning the model or how to improve its performance on your datasets! 🔍👇 Also, check out the links in the description for more related tutorials on regression techniques. Let’s optimize those models! 💻📈 #SGDRegressor #GradientDescent #MachineLearning #DataScience #LinearRegression #Optimization #optimisation
🚀 Did you find this video helpful? 🚀 Regularization techniques like Ridge, Lasso, and Elastic Net can be game-changers in improving your Linear Regression models. Let me know in the comments which technique you prefer, or if you have any questions about how to apply them to your own projects! 🔍👇 Also, don’t forget to check out the links in the description for more in-depth videos on each regularization method! 👍 #MachineLearning #DataScience #RidgeRegression #LassoRegression #ElasticNet #ModelOptimization
Amazing ! Thanks for this video
Glad you liked it!
Highly informative and very well explained. I always struggled to understand this concept but this video was so easy to follow and very well explained 👌
Glad it was helpful!
sounds creepy
It really is ..
Great conceptualising the techniques, might I suggest to include code base videos as well for us?
I sure will, can you please review my video on linear regression python tutorial and share your thoughts?
Thanks for watching! If you're new to Linear Regression, be sure to check out our previous video where we cover the fundamental concepts in depth. Understanding these basics will make following along with this mathematical breakdown even easier.
Thank you so much for watching! 🎉 In this video, we dive into the concepts of Linear Regression Feel free to leave any questions or comments below-I’d love to hear your thoughts and help with any clarifications you might need. Stay tuned for more exciting videos!