Metadata-Version: 2.1
Name: ml_tutor
Version: 1.0.1
Summary: ML Tutor : Learn Machine Learning while never leaving the conform of your Python IDE (Jupyter Notebook or Google Colab)
Home-page: https://github.com/lucko515/ml_tutor
Author: Luka Anicin
Author-email: luka.anicin@gmail.com
License: MIT
Description: # ML Tutor v1.0.1
        
        ML Tutor is a Python library created to help people learn Machine Learning (ML). 
        
        Machine Learning (ML) is pretty hard and especially if you are just starting (been there done that)! 
        I've created this library to help anybody interested in learning ML. ML Tutor provides visual training 
        for every algorithm inside it so you can visualize what's happening with your data in real-time!
        Besides that, for every algorithm, there is a theory on how it works and interview questions. 
        
        Happy learning! ^_^
        
        Use ML Tutor if you are looking to:
        
        - Learn most popular Machine Learning algorithms directly from Jupyter Notebook or Google Colab
        - Visualize what's happening with your data (Educational purpose only)
        
        
        ## Usage
        
        To demonstrate what you can do with ML Tutor, we will need a dataset.
        You can use your own dataset or some classic dataset (such as [Iris](https://archive.ics.uci.edu/ml/datasets/iris)).
        
        Let's use the Iris dataset from the Sklearn library and split it into the training and testing subsets.
        
        ```python
        from sklearn.datasets import load_iris
        from sklearn.model_selection import train_test_split
        
        dataset = load_iris()
        X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.2)
        ```
        
        ### Training/Prediction
        
        #### Clustering
        For the clustering example, let's import KMeans and demonstrate how to use it with the ML Tutor library.
        Notice that you can train/test it just like any `sklearn` algorithm.
        
        Each algorithm has several arguments you can provide, but the unique one across all of them is `visual_training`. 
        If you set this to `True`, you will see the whole training process inside your IDE.
        
        ```python
        from ml_tutor.clustering.kmeans import KMeans
        
        clf = KMeans(n_clusters=3, visual_training=True)
        clf.fit(X_train)
        ```
        ![](images/kmeans-vt.gif)
        
        #### Classification
        
        For the classification example, let's use KNeighbourClassifier (KNN).
        
        Because the KNN just stores data when you call the `.fit()` function on it, the visualization part comes in the __prediction__ time.
        
        ```python
        from ml_tutor.classification.knn import KNeighbourClassifier
        
        clf = KNeighbourClassifier(n_neighbors=5, visual_training=True, number_of_visual_steps=2)
        clf.fit(X_train, y_train)
        
        predictions = clf.predict(X_test)
        ```
        
        ![](images/knn.gif)
        
        ### How an algorithm works? (theory)
        
        Every algorithm has method `.how_it_works()` which generates a blog post directly inside your IDE.
        Every blog is written by somebody from the community, not myself, and in the end, they get a shout out for the great material.
        ```python
        from ml_tutor.classification.knn import KNeighbourClassifier
        
        clf = KNeighbourClassifier()
        clf.how_it_works()
        ```
        
        ![](images/th.gif)
        
        #### What if I prefer video instead of reading tutorial?
        
        I've got you covered! Just place `video=True` inside the method `how_it_works()`, and it will open a YouTube video for you.
        
        ```python
        from ml_tutor.classification.knn import KNeighbourClassifier
        
        clf = KNeighbourClassifier()
        clf.how_it_works(video=True)
        ```
        
        ![](images/thv.gif)
        
        
        ### Interview questions
        
        If you call `.interview_questions()` on any algorithm, it will generate resources with interview questions for the algorithm.
        
        ```python
        from ml_tutor.classification.knn import KNeighbourClassifier
        
        clf = KNeighbourClassifier()
        clf.interview_questions()
        ```
        
        ![](images/inter-q.png)
        
        ### Sklearn code
        
        Since this is the library for education and not for production, you'll need to learn how to use these algorithms with the battle-tested library `sklearn`. Just call `.sklearn_version()` on any algorithm, and it will generate code for you!
        
        NOTE: For now, this method only works in Jupyter Notebook!
        
        ```python
        from ml_tutor.classification.knn import KNeighbourClassifier
        
        clf = KNeighbourClassifier()
        clf.sklearn_version()
        ```
        
        ![](images/sklearn.gif)
        
        ## Supported IDEs
        
        For now this library is fully supported for `Jupyter Notebook` and partially supported for `Google Colab` 
        (read `Sklearn code` section for more details). 
        
        ## Installation
        
        ### Installation with `pip`
        
        You can install ML Tutor directly from the PyPi repository using `pip` (or `pip3`): 
        
        ```bash
        pip install ml-tutor
        ```
        
        ### Manual installation
        
        If you prefer to install it from source:
        
        1. Clone this repository
        
        ```bash
        git clone https://github.com/lucko515/ml-tutor
        ```
        
        2. Go to the library folder and run
        
        ```bash
        pip install .
        ```
        
        ## TODO
        
        If you want to contribute to the ML Tutor, here is what's on the TODO list:
        
        - [ ] `.sklearn_version()` is not working in Google Colab
        - [ ] Logistic Regression visualization needs a re-do, currently it's not showing how the classification lines moves over time
        - [ ] Interview questions should be added to each algorithm (take the `knn.py` as a reference)
        - [ ] Visualization export to `.gif` and/or `.mp4`
        - [ ] Additional algorithms (e.g. NaiveBayes)
        - [ ] Support for other IDEs (e.g. regular Python Shell)
        
        ## Contact
        
        Add me on [LinkedIn](https://www.linkedin.com/in/luka-anicin/).
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3
Description-Content-Type: text/markdown
