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

1. 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)

2. 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:

• Bias Variance Tradeoff Part-1 (Direct Link: https://www.youtube.com/watch?v=UxSCxX6KNQQ&t=315s )

3. 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)

4. 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)

5. 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)

6. 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)

7. 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)