GitHub - corinabioinformatic/MachineLearningResources: Machine Learning tutorial as a question-answer practical exercises. Based on the ML Johns Hopkins University course at Coursera

corinabioinformatic / MachineLearningResources Public

Notifications You must be signed in to change notification settings
Fork 0
Star 0

Machine Learning tutorial as a question-answer practical exercises. Based on the ML Johns Hopkins University course at Coursera

0 stars 0 forks Branches Tags Activity

Star

Notifications

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
.ipynb_checkpoints		.ipynb_checkpoints
.gitignore		.gitignore
Machine Learning Resources I (step_by_step).ipynb		Machine Learning Resources I (step_by_step).ipynb
Machine Learning Resources II (caret_package).ipynb		Machine Learning Resources II (caret_package).ipynb
Machine Learning Resources III (Predicting with trees, Random forest and model based predictions).ipynb		Machine Learning Resources III (Predicting with trees, Random forest and model based predictions).ipynb
Machine Learning Resources IV (Regularized Regression and Combining Predictors).ipynb		Machine Learning Resources IV (Regularized Regression and Combining Predictors).ipynb
README.txt		README.txt
Random forest in bussiness (data driven decision-making example).ipynb		Random forest in bussiness (data driven decision-making example).ipynb

Repository files navigation

#(1) Installing Jupyter and R kernel to create notebooks.
https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook##UseJupyter
#(2) Running Jupyter notebook by mean of Ipython command.
https://discuss.analyticsvidhya.com/t/how-to-run-r-on-jupyter-ipython-notebooks/5512 

#NOTE:Once jupyter installed by mean of conda environment, just run:
ipython notebook
#(3) it has installed all the required libraries to plot the graphs.
#(4) Finally push this .ipnby file to a github repositorie and get the link to visualize the notebook in jupyter nbviewer.