Machine Learning
This site is dedicated to the simplest video tutorials on advanced to basic topics of Machine Learning. I have used my knowledge and experience to prepare these tutorials. All feedback and suggestions are welcome (email me at nirajrkumar@gmail.com or nirajrkumar@yahoo.com).
As, scientific development is an endless process, so I will keep updating it. Clicking on the link will drive you to the YouTube page for related content. Or You can use the link: https://www.youtube.com/c/DrNirajRKumar
- Stacking “Stacked Generalization” (A winning Ensemble Classification Strategy).
Content:
- Stacking Vs. Other Ensemble Classification Strategies.
- Stacking Basics.
- Stacking Step-by-Step
Video Link:
Stacking “Stacked Generalization” (A winning Ensemble Classification Strategy) (Direct Link: https://www.youtube.com/watch?v=sgA-arA0lzg&t=120s)
- Bias Variance Tradeoffs.
Content:
- Basics of Bias and Variance
- Bias and variance using bulls-eye diagram.
- Bias Variance Tradeoff and Algorithm Design Strategies
Video Link:
- Bagging Technique - Random Forest based Classification.
Content:
- Bagging Technique
- Bootstep Aggregation
- Step-by-Step explanation of Random Forest
Video Link:
Bagging Technique - Random Forest based Classification (Direct Link: https://www.youtube.com/watch?v=ajTc5y3OqSQ&t=151s)
- Boosting Technique-1: Gradient Boosting Simplified (Classification).
Content:
- Basics of Boosting
- Step-by-step explanation of Gradient Boosting
Video Link:
Gradient Boosting Simplified (Classification) (Direct Link: https://www.youtube.com/watch?v=BX9szMu5GEE&t=1212s)
- Boosting Technique-2: XGBoost Simplified (Classification).
Content:
- Part-1
- Boosting Algorithm Basics.
- XGBoost Classification Basics.
- XGBoost Tree Construction Basics
- Part-2
- XGBoost Tree Construction step-by-step with Example.
- Classification using XGBoost.
Video Link:
XGBoost Simplified: Part-1 (Classification) (Direct Link: https://www.youtube.com/watch?v=p8mkur7iNDA)
XGBoost Simplified: Part-2 (Classification) (Direct Link: https://www.youtube.com/watch?v=9wUzP-FJnQA&t=25s)
- Kullback-Leibler Divergence (KL Divergence).
Content:
- Part-1:
- Basics of KL-Divergence (Discrete case)
- Using Smoothing with KL-Divergence (based on absolute discounting)
- Part-2:
- Using the KL-Divergence as a distance metric to compute the similarity between documents.
- Part-3:
- KL-Divergence and Mutual Information,
- Computation of mutual information between short texts.
Video Link:
Kullback-Leibler Divergence (KL Divergence) Part-1 (Direct Link: https://www.youtube.com/watch?v=PinkT8X4cGM)
Kullback-Leibler Divergence (KL Divergence) Part-2. (Direct Link: https://www.youtube.com/watch?v=J1k1-oWoZ-0)
Kullback-Leibler Divergence (KL Divergence) Part-3. (Direct Link: https://www.youtube.com/watch?v=kCit6tDuZJg)
- Computing Average F1, Macro F1 and Micro F1 for Multi-Class Classification.
Content:
- This tutorial provides step-by-step discussion on -
- "How to Compute Average F1, Macro F1 and Micro F1 for Multi-Class Classification"
Video Link:
Computing Average F1, Macro F1 and Micro F1 for Multi-Class Classification (Direct Link: https://www.youtube.com/watch?v=L2tBh63ggt0)
8. Multivariate Time Series Forecasting Using Deep Learning.
Content:
- Part-1:
- Different Types of Multivariate Time Series Forecasting Strategies.
- Multivariate Multi-Step Multi-Output Time series Forecasting
- Strategy to prepare dataset.
- How to write code?
- Part-2:
- Multivariate Single-Step Multi-Output Time series Forecasting
- Strategy to prepare dataset.
- How to write code?
- Strategy for the Future Enhancements.
Video Link:
Multivariate Time Series Forecasting Using Deep Learning [Part-1] (Direct Link: https://youtu.be/xaQpLz6QkVQ)
Multivariate Time Series Forecasting Using Deep Learning [Part-2]. (Direct Link: https://youtu.be/DLzaG4SW4pM)