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Python for Machine Learning

Table of contents:

  1. Numpy
  2. Matplotlib - basics of the Visualization
  3. Pandas & Seaborn - working with data
  4. Linear regression
  5. Classification
  6. Advanced topics in Regression & Classification
  7. Classification of MNIST dataset: Logistic Regression & SVM
  8. Implementation of Algorithms in Numpy: kNN & Logistic Regression
  9. Project: K-Means
  10. PCA

Reference

Prince, Simon J. B., Understanding Deep Learning. Retrieved from https://udlbook.github.io/udlbook/

LMU Munich team, Introduction to Machine Leanring https://slds-lmu.github.io/i2ml/

Andriy Burkov, The Hundred-Page Language Models Book, https://themlbook.com/

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