/Machine-Learning-Lab

Machine Learning Lab - colab notebooks

Primary LanguageJupyter NotebookMIT LicenseMIT

Contains material for Machine Learning Lab.

  1. Python essentials for machine learning
  2. Feature engineering in machine learning
  3. Implementation of linear regression
  4. Implementation of decision tree
  5. Implementation of Bayesian Network
  6. Implementation of Principle component analysis
  7. Implementation of K-Means clustering Algorithm
  8. Implementation of expectation maximization algorithm

How to contribute?

Please feel free to contribute. Please make sure the code is clear, variables are well named and the comments are proper.

You can contribute in either of the ways.

  1. Add material related to the topic
  2. Add code related to the topic with proper documentation, references and explaination.
  3. Add code in question answer format. (Example practical question and corresponding answer code)
  4. (for students) Add practicals code, writeups conducted in your colleges related to this topic.

Note for college students

Please feel free to contribute. Please remember that this repository is for helping students and not encouraging in any malpractises. Anti-plagarism measures taken by the college will detect this code and strict action will be taken against you, hence refrain from copying any part of this material. Please do not use this repository to just copy paste the code or the solutions. Try to understand this on your own and then try it out. For any doubts, please raise an issue for seeking help.


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