fenago/machine-learning repository overview

Course Name Machine Learning

Docker Image fenago/machine-learning

Run Image docker run -d --restart=always --user root -p 80:80 --name ml -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes fenago/machine-learning jupyter lab --port 80 --allow-root

Note: You can also run multiple instances of jupyterLab by changing container name and port ( -p <update-host-port>:80) in the above command.

Commit & Push Changes

docker commit ml fenago/machine-learning

docker push fenago/machine-learning

Labs

Labs for this course are available at path shared below. Elev8ed Notebooks (powered by Jupyter) will be accessible at the port given to you by your instructor.

1. ⁠What is machine learning? (Read Only)

    * http://<host-ip>/lab/workspaces/lab1_introduction
   

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2. ⁠Types of machine learning (Read Only)

    * http://<host-ip>/lab/workspaces/lab2_types
   

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3. ⁠Drawing a line close to our points: linear regression

    * http://<host-ip>/lab/workspaces/lab3_regression
   

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4. ⁠Using lines to split our points: the perceptron algorithm

    * http://<host-ip>/lab/workspaces/lab4_perceptron
   

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5. ⁠A continuous approach to splitting points: logistic regression

    * http://<host-ip>/lab/workspaces/lab5_logistic
   

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6. ⁠Using probability to its maximum: naive Bayes algorithm

    * http://<host-ip>/lab/workspaces/lab6_naive_bayes
   

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7. ⁠Splitting data by asking questions: decision trees

    * http://<host-ip>/lab/workspaces/lab7_decision_trees
   

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8. ⁠Predicting Categories with Naive Bayes and SVMs

    * http://<host-ip>/lab/workspaces/lab8_support_vector
   

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9. ⁠Combining models to maximize results: ensemble learning

    * http://<host-ip>/lab/workspaces/lab9_ensemble_learning
   

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10. ⁠Testing, Overfitting, Underfitting

    * http://<host-ip>/lab/workspaces/lab10_testing
    

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11. ⁠Neural Networks

    * http://<host-ip>/lab/workspaces/lab11_neural_networks
    

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12. ⁠End to end example

    * http://<host-ip>/lab/workspaces/lab12_example
    

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