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A day to day plan for this challenge (50 Days of Machine Learning) . Covers both theoretical and practical aspects
| Date | Stars |
|---|---|
| 2026-07-24 | 257 |
| 2026-07-25 | 257 |
| 2026-07-28 | 257 |
| 2026-07-30 | 257 |
| 2026-07-31 | 257 |
| 2026-08-06 | 257 |
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# 50-Days-of-ML A day to day plan for this challenge. Covers both theoritical and practical aspects. I have build [ __Docker Image__](https://hub.docker.com/r/prakhar21/ml-utilities/) with all the required dependencies till __Day 21__. Feel free to use it by pulling it using -> __docker pull prakhar21/ml-utilities__ Please see [__Deep Work__](https://www.quora.com/What-is-the-one-skill-that-if-you-have-it-will-completely-change-your-life/answer/Shashank-Shekhar-221) which compliments our challenge and increases productivity. You can follow me on [__@Medium__](https://medium.com/@prakhar.mishra) for interesting blog articles. ## Day-1 (31st July, 2018) * Learn about Pandas. [See Videos(1-5)](https://www.dataschool.io/easier-data-analysis-with-pandas/) * Learn in general about ML [See Video (Blackbox Machine Learning)](https://www.youtube.com/watch?v=MsD28INtSv8) * Read/Practice [Day-1 and Day-2](https://github.com/Avik-Jain/100-Days-Of-ML-Code) * See [Intro to Linear Regression](https://www.youtube.com/watch?v=zPG4NjIkCjc) * Read [LR Docs](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) ## Day-2 (1st August, 2018) * Learn about Pandas. [See Videos(6-10)](https://www.dataschool.io/easier-data-analysis-with-pandas/) * Learn in general about ML [See Video (Case Study: Churn Prediction)](https://www.youtube.com/watch?v=kE_t3Mm8Z50) * Read/Practice [Day-3](https://github.com/Avik-Jain/100-Days-Of-ML-Code) * See [Data Spread](https://www.khanacademy.org/math/probability/data-distributions-a1/summarizing-spread-distributions/v/range-variance-and-standard-deviation-as-measures-of-dispersion) * Andrew Ng [See Videos (1-3)](https://www.youtube.com/watch?v=-la3q9d7AKQ&list=PLNeKWBMsAzboR8vvhnlanxCNr2V7ITuxy) ## Day-3 (2nd August, 2018) * Learn about Pandas. [See Videos(11-15)](https://www.dataschool.io/easier-data-analysis-with-pandas/) * Learn in general about ML [See Video (Statistical Learning Theory)](https://www.youtube.com/watch?v=rqJ8SrnmWu0) * Read/Practice [Day-4 and Day-8](https://github.com/Avik-Jain/100-Days-Of-ML-Code) * Visualization in Python [See Official Docs](https://matplotlib.org/users/pyplot_tutorial.html) ## Day-4 (3rd August, 2018) * Learn about Pandas. [See Videos(16-18)](https://www.dataschool.io/easier-data-analysis-with-pandas/) * Read [KNN-1](https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/) * Read [KNN-2](https://medium.com/@adi.bronshtein/a-quick-introduction-to-k-nearest-neighbors-algorithm-62214cea29c7) ## Day-5 (4th August, 2018) * Learn about Pandas. [See Videos(19-22)](https://www.dataschool.io/easier-data-analysis-with-pandas/) * Read/Practice [Day-7](https://github.com/Avik-Jain/100-Days-Of-ML-Code) * General read on [Medium](https://blog.usejournal.com/cracking-eaadhar-password-in-3-seconds-with-maths-9533c8e8f9c2) ## Day-6 (5th August, 2018) * Learn about Pandas. [See Videos(23-26)](https://www.dataschool.io/easier-data-analysis-with-pandas/) * Implementing KNN * Read/Practice [Day-12](https://github.com/Avik-Jain/100-Days-Of-ML-Code) * KNN-Sklearn [See Official Docs](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) ## Day-7 (6th August, 2018) * Learn about Numpy. [Read this](https://www.dataquest.io/blog/numpy-tutorial-python/) * [Naive Bayes - 1](https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/) * [Naive Bayes - 2](https://medium.com/machine-learning-101/chapter-1-supervised-learning-and-naive-bayes-classification-part-1-theory-8b9e361897d5) * [Naive Bayes - 3](https://machinelearningmastery.com/naive-bayes-for-machine-learning/) * [Naive Bayes - 4](https://www.youtube.com/watch?v=6xBU74VWEuE) ## Day-8 (7th August, 2018) * [Lime](https://github.com/marcotcr/lime) * [Building Trust in ML models](https://www.analyticsvidhya.com/blog/2017/06/building-trust-in-machine-learning-models/) * [Interpretable ML models](https://www.oreilly.com/le
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