> For the complete documentation index, see [llms.txt](https://opencampus.gitbook.io/opencampus-machine-learning-program/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/intermediate-machine-learning/week-1/cousera-videos.md).

# Cousera Videos

Watch them all😊

1. Why Machine Learning is exciting

{% embed url="<https://drive.google.com/file/d/1yutz-vYXjYEWUSWtXkvV8xDskmQXCC2x/view?usp=sharing>" %}

2. What is Machine Learning?

{% embed url="<https://drive.google.com/file/d/1qeT_eYRxV1TsDiSQ3SB3GIA2CJa6uRUv/view?usp=sharing>" %}

3. Logistic Regression

{% embed url="<https://drive.google.com/file/d/1T3Xfg_CQ48Xqk9xWatytlnUPyzoo6iGv/view?usp=sharing>" %}

4. Interpretation of Logistic Regression

{% embed url="<https://drive.google.com/file/d/1uXqbBK64CC8MBiMn4osBwt8jCnvK5fhz/view?usp=sharing>" %}

5. Motivation for Multilayer Perceptron

{% embed url="<https://drive.google.com/file/d/151E6-NwvEOvhsrnAQpJA8ili6kWK_tdv/view?usp=sharing>" %}

6. Multilayer Perceptron Concepts

{% embed url="<https://drive.google.com/file/d/1k_2h-gbvDQqHy_MtDmDznRlJXbTud3wj/view?usp=sharing>" %}

7. Multilayer Perceptron Math Model

{% embed url="<https://drive.google.com/file/d/15EW_H-QvJmT5jzykIyLZU1b-ICNEVhw0/view?usp=sharing>" %}

8. Deep Learning

{% embed url="<https://drive.google.com/file/d/1fKXxu6YTCTwWUI-x0BJPD728L1M113Ca/view?usp=drive_link>" %}

9. Example: Document Analysis

{% embed url="<https://drive.google.com/file/d/1C7xoOzrTIFMJsJLvuq-1reoBJ6r3vm4I/view?usp=sharing>" %}

10. Interpretation of Multilayer Perceptron

{% embed url="<https://drive.google.com/file/d/1ew58mjD5xaDJ-EW_6YaKvjjWUS_kYvGR/view?usp=sharing>" %}

11. Transfer Learning

{% embed url="<https://drive.google.com/file/d/1xppD2Qint6RVrka5tq8-BrwChYY2IaGA/view?usp=drive_link>" %}

12. Model Selection

{% embed url="<https://drive.google.com/file/d/15PawNrnfBAgiBUrFbe72ZamZbi30RXlq/view?usp=sharing>" %}

13. Early History of Neural Networks

{% embed url="<https://drive.google.com/file/d/1lJ4j9s85CxwxJYd9uxdQygeT6zTay7JH/view?usp=sharing>" %}

14. Hierarchical Structure of Images

{% embed url="<https://drive.google.com/file/d/10LT8uW2IcMJUQXVqoJptUf6-tjs808yL/view?usp=drive_link>" %}

15. Convolutional Filters

{% embed url="<https://drive.google.com/file/d/1n0xj2Qn1tFtj1tBJH4rl83t_zufAMRyB/view?usp=sharing>" %}

16. Convolutional Neural Networks

{% embed url="<https://drive.google.com/file/d/12wVBRhNPtMjuDQ3ClqBSAz0tnIhHCWMM/view?usp=sharing>" %}

17. CNN Math Model

{% embed url="<https://drive.google.com/file/d/1Ha67tUoqQKv1-tpt8eayHIdZqMp8Xh2A/view?usp=sharing>" %}

18. How the Model learns

{% embed url="<https://drive.google.com/file/d/1ArHvg0r9GstifFnAz_MmBtWEnECBgUbU/view?usp=sharing>" %}

19. Advantages of Hierachical Features

{% embed url="<https://drive.google.com/file/d/1sK36bq6Zn0wqY5ouibiIu82qWYd58hl0/view?usp=sharing>" %}

20. CNN on Real Images

{% embed url="<https://drive.google.com/file/d/1CXKocOzkELBQakORjx7hZrNAXOZeUjdw/view?usp=sharing>" %}

21. Applications and Use in Practice

{% embed url="<https://drive.google.com/file/d/1KXcqxS3sVchgZheTclUKd2DwjDN-Vy0o/view?usp=sharing>" %}

22. Deep Learning and Transfer Learning

{% embed url="<https://drive.google.com/file/d/1m4TpBLnJ2ZQGztZJx9apqOxuc5OWL4pD/view?usp=sharing>" %}

Done!


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