kaushal1011/machine-learning

Practice/Notes of Machine Learning

machine-learning

Collaborators:

• Kaushal Patil

• Dhruvil Dave

Guide to AI with Python(And R)

• Machine Learning

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src

• Practical Introduction
• Training Models 1
• Training Models 2
• Ensemble Methods
• Dimensionality Reduction

theory

-Introduction

-Regression

How to Ml

• How to Ml

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Information about Source (Hyperlinked to Python Notebooks;Scroll for html links)

• Practical Introduction Contains basic information about approaches to make machine learning models.

• Training Models 1 Contains practical approaches to Following Training Models

  • Contents
    • Linear Regression
      • Normal Equation
      • Gradient Descent
      • Regularized Models
    • Logistic Regression
    • Decision Tress
      • Classification
      • Regression

• Training Models 2 Contains practical approaches to following Training Models

• Ensemble Methods Contains notes and explainations on following ensemble methods:

  • Contents
    • Voting Classifier
    • Bagging vs Pasting
    • Random Patches and Random Subspaces
    • Random Forests
    • Feature Importance
    • Boosting
      • AdaBoost
      • Gradient Boosting
    • Stacking

• Dimensionality Reduction Contains Approaches to reduce dimension of data before trainging a model on it

• Unsupervised Learning Contains Unsupervised Learning Algorithms

• theory folder constains theory about Machine Learning

  • Introduction contains Statistical Theory(In depth) about machine learning

  • Regression constains theory about simple and multiple linear regression

• Data Visualisation Contains introductory practical insights on plotting with Seaborn.(Reference Kaggle MicroCourse on Data Visualisation)

• S1Regresssion is an example of how to apply linear regression to a dataset. The analysis is dangerously incomplete as of now (10/10/19).

• tf_introduction is guide to basic operations of tensorflow.

• Essental Statistics and Probability is the guide to essentials of statistics and probability required for data science and engineering.

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Please use the rendered HTML file directly from the bin/ folder if to avoid any malfunctioning.

Please use the commit.sh file to commit the changes and then push to the remote to maintain a common format of commit messages

conda.sh file sets up the environment required to run the codes

machine-learning's Issues

Regarding Hacktoberfest Contribution

You can add either add to any of the topics in the supplied notebooks or create new notebooks with new topics. make sure to discuss here in issues before you decide to contribute! 😸 👍

Images and Old Html Exports

some of the images have ".png" instead of ".PNG " in notebooks and html exports need to be done again.