Applied-ai-course

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Applied-ai-course

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Applied ai course is a online platform to learn about data science. In this repository include assignments ,projects, coding etc. done in the course

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# Applied-ai-course
[Applied ai course](https://www.appliedaicourse.com/) is a online platform to learning data science. In this repository include assignments ,projects done in the course as part of their curriculum. These repository include basic python programming to advanced ml and dl problems

## Contents
1. [Python user defined functions](https://github.com/basilkjose/Applied-ai-course/tree/main/FUNCTION_)
2. [Pandas exercise](https://github.com/basilkjose/Applied-ai-course/tree/main/pandas)
4. [Python mathematical exercise](https://github.com/basilkjose/Applied-ai-course/tree/main/1.python%20mandatory)
5. [Exploratory data analysis](https://github.com/basilkjose/Applied-ai-course/tree/main/2.eda)
6. [TF-IDF implementation from scratch](https://github.com/basilkjose/Applied-ai-course/tree/main/3.tfidf%20implementation)
7. [Random search cv from scratch](https://github.com/basilkjose/Applied-ai-course/tree/main/4.random%20search%20cv)
8. [Perfromance metrics without using sklearn](https://github.com/basilkjose/Applied-ai-course/tree/main/5.perfromance%20metrics)
9. [Naive Bayes Classifier on donorschoose dataset](https://github.com/basilkjose/Applied-ai-course/tree/main/6.naive%20bayes%20on%20donar%20dataset)
10. [Implementation of SGD without sklearn](https://github.com/basilkjose/Applied-ai-course/tree/main/7.Implement%20SGD%20Classifier%20with%20Logloss%20and%20L2%20regularization%20Using%20SGD%20without%20using%20sklearn)
11. [Behaviour of linear models](https://github.com/basilkjose/Applied-ai-course/tree/main/8.Behavior%20of%20Linear%20Models)
12. [Decision tree on donarchoose dataset](https://github.com/basilkjose/Applied-ai-course/tree/main/9.Apply%20Decision%20Trees%20on%20Donors%20Choose%20dataset)
13. [Implementation of Random forest and application of bootstrap samples](https://github.com/basilkjose/Applied-ai-course/tree/main/10.Application%20of%20Bootstrap%20samples%20in%20Random%20Forest)
14. [GBDT on donarchoose dataset](https://github.com/basilkjose/Applied-ai-course/tree/main/11.Apply%20GBDT%20on%20Donors%20Choose%20dataset)
15. [Clustering on Graph dataset](https://github.com/basilkjose/Applied-ai-course/tree/main/12.clustering%20on%20graph%20dataset)
16. [Recommendation Systems and Truncated SVD](https://github.com/basilkjose/Applied-ai-course/tree/main/13.Recommendation%20Systems%20and%20Truncated%20SVD%20SGD)
17. [Facebook Recomendation as a classification task](https://github.com/basilkjose/Applied-ai-course/tree/main/14.facebook)
18. [Implementation of Back Propagation from scratch](https://github.com/basilkjose/Applied-ai-course/tree/main/15.back%20prop)
19. [Working with Tensor flow 2.0 callbacks](https://github.com/basilkjose/Applied-ai-course/tree/main/16.Working%20with%20Callbacks)
20. [Document classification using Transfer Learning](https://github.com/basilkjose/Applied-ai-course/tree/main/17.transfer%20learning)

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