Metadata-Version: 2.1
Name: PyDojoML
Version: 0.4.2
Summary: A General Purpose Machine Learning Library for Python
Home-page: https://github.com/VIVelev/PyDojoML
Author: Victor Velev
Author-email: velev.victor@yahoo.com
License: UNKNOWN
Description: # PyDojoML
        A General Purpose Machine Learning Library for Python
        
        ## A quick taste of PyDojoML
        
        ### How to install
        You can easily install it with `pip`.<br>
        Copy-paste this in your terminal and run it.
        ```
        pip install pydojoml
        ```
        Good job, now it's time we rock-and-roll!<br>
        
        ### Simple Linear Regression example:
        ```
        import numpy as np
        from dojo.linear import LinearRegression
        
        # Let's create some data to fit the model to.
        X = np.random.randn(100_000, 100)
        y = X @ np.random.rand(100)
        
        # Building the model.
        linear_reg = LinearRegression()
        
        # Fitting the model is as easy as a call of a method.
        linear_reg.fit(X, y)
        
        # Now lets predict.
        prediction = linear_reg.predict(X[:20, :])
        
        ```
        
        ## Dojo's ingredients
        ### Linear Models
          - Linear Regression
          - LASSO
          - Ridge
          - Logistic Regression
        
        ### Deep Neural Networks
          - Layers:
            - Dense
            - Activation
        
        ### Activation functions
          - Linear
          - Sigmoid
          - Softmax
          - TanH
          - ReLU
          - Leaky ReLU
        
        ### Losses
          - Squared Error
          - Cross Entropy
        
        ### Optimizers
          - Stochastic/Batch/Mini-batch Gradient Descent
          - Momentum
          - RMSprop
          - Adam
        
        ### Regularizer
          - L1
          - L2
        
        ### Tree Models
          - Classification And Regression Trees (CARTs)
          - Extra-Trees
          
        ### Support Vector Machines
          - C-SVM
          - Epsilon-SVM
          - Nu-SVMs
        
        ### Bayes
          - Naive Bayes algorithm
          
        ### Ensemble Learning
          - AdaBoost
          - Model Stacking
          
        ### Clustering
          - Hierarchical Clustering
          - K-Means algorithm
          
        ### Anomaly detection
          - Univariate and Multivariate Gaussian Distribution
          
        ### Dimensionality Reduction Techniques
          - Principal Component Analysis
          - Linear Discriminant Analysis
        
        ### Preprocessing
          - Encoders:
            - Label Encoder
            - OneHot Encoder
          - Scalers:
            - Normalizer
        
        ### Various metrics
          - classification
          - regression
          - clustering
          
        ### Model evaluation utils
          - Train-Test splits
          - K-Fold Cross Validation
        
        ### Plotting
          - Decision Boundary plotter
          
        ### Data Preprocessing utils
          - encoders
          - normalizers
          - scalers
        
        ### Natural Language Processing utils
          - TF-IDF
        
        ### Recommender Systems
          - Content Based
          - Collaborative Filtering
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
