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Artificial Intelligence Tool Kit Mark Two.

Project description

AITools

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Artificial Intelligence Tool Kit Mark Two.

Features

  • N-GRAM

    • Predict the next word of the sentence that further can be use for predicting the entire sentence.
    • Generation of text like a paragraph/article on Health or environment or any other topic (given relevant data).
    • Generation of the next GOT books!!!.
    • Next Phase - Use of bayesian statistics and more nice probability models for getting the best results.

Look For: Demo file n_gram.py

  • K-MEANS

    • Creates clusters and classify new nodes into those clusters.
    • Efficient and easy to implement.
    • Next Phase - Multiple algorithms implementation.

Look For: Demo file k_means.py and k_means_color_cluster.py

  • Logistic Regression

    • From probability of event happening or not to the next Data point, it is a very versatile algorithm.
    • Simple Implementation and Training routines.
    • Uses Gradient Descent and Sigmoid Function.
    • Next Phase - Improvements in Gradient Descent is still in progress.

Look For: Demo file logistic_regression.py

  • Naive Bayes

    • Algorithm based on Bayesian statistics, which is capable of creating classifications like an intent classifier or sentiment analysis engine(A true complex one not the Pathetic Twitter thing).
    • Created using little bit of probability and bayesian statistics with a hint of programming and with a lot of love.
    • Next Phase - Flexibility and more control on different algorithm.

Look For: Demo file naive_bayes.py

  • Decision Tree

    • Algorithm based on Gini logic and info gain for prediction.
    • Trees can be very use full but may lead to aver fitting easily Be Cautious.
    • Next Phase - Flexibility and more control on different algorithm.

Look For: Demo file decision_tree.py

  • Pre Processing

    • Utils for Pre Processing Data.
    • Next Phase - More Cool Functions.

Look For: Demo file pre_processing.py

  • Others

    • In build Mathematics Util.
      • Vector Math, Probability, and some Statistic
      • Util will be upgraded as per the need.
    • Gradient Descent.
      • Entire Gradient Descent algorithm combined into one.
      • Usability Docs will be Provided Soon.
  • Next Updates

    • Improving all algorithms and giving proper documentation on usage.
    • Providing more user control on algorithm selection and output handling.
    • Detailed Demo files.
    • Sudo codes and algorithm explanations.
    • Will be available to lower version( > 2.7).

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.0 (2019-04-14)

  • GitHub Setup.
  • Travis CI Build Added.
  • Documentation skeleton added on ReadTheDocs.
  • First release on PyPI.

0.1.0 (2019-04-21)

  • N-Gram algorithm version one completed.
  • First level of documentation.
  • N-Gram demo files added.

0.1.0 (2019-04-25)

  • Gradient Descent Created.
  • Word Stemmer Added(Still needs improvements and not been used through out the project).

0.1.0 (2019-04-28)

  • Logistic Regression Added.
  • Demo file added.

0.1.2 (2019-05-01)

  • Naive Bayes Created.

0.1.2 (2019-05-01)

  • Pre Processing Added.

0.1.2 (2019-05-01)

  • First Level of Documentation Created.

0.1.2 (2019-05-27)

  • Lost Track of most of the things.
  • Finally We have following
    • K Means
    • Logistic Regression
    • N Gram
    • Naive Bayes
    • Pre Processing
    • SVM in progress.

0.1.3 (2019-06-24)

  • Decision Tree Added(Gini and Impurity Method).
  • Previous algorithm improvements are in progress.

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