Roadmap to Data Science and Machine Learning (RDScML)

An exhaustive study guide to learn machine learning concepts from scratch. It focuses on preparing an individual through detailed exploration and hands-on implementation of the concepts. The concepts are to be learned by solving the issues that are brilliantly crafted by experienced personnel. The solutions submitted via pull requests will be reviewed carefully and constructive feedback will be given. Post this, there are three major projects:

Great Energy Prediction
COVID-19 Visualization
Quora Insincere Questions Classification

These projects on solving would reflect the detailed knowledge and understanding of three main data science aspects:

Algorithmic Knowledge
Building Visual Deliverables
Natural Language Processing Applications

Timeline

The following timeline can be followed by any learner:

Part I

Week 1: Getting started with Python
Week 2: Manipulating data using Pandas and NumPy
Week 3: Data visualisation techniques
Week 4: Concept Application I: Solve the Great Energy Prediction problem statements (perform data exploration only)

Part II

Week 5: Probability and Statistics
Week 6: Linear Models
Week 7: Regularisation
Week 8: Concept Application I: Complete solving the Great Energy Prediction problem statement

Part III

Week 9: EDA and Feature engineering
Week 10: Classification models
Week 11: Decision Trees
Week 12: Concept Application II: Solve COVID-19 visualisation problem statement

Part IV

Week 13: Ensemble methods
Week 14: Clustering Algorithms
Week 15: Support vector machines
Week 16: Introduction to NLP

Part V

Week 17: Topic modelling
Week 18: Sentiment analysis
Week 19: Concept Application III: Solve the problem statement on Quora Insincere Questions Classification
Week 20: Concept Application III: Solve the problem statement on Quora Insincere Questions Classification (cont.)

Contribution Guidelines

To keep the project structure and review process manageable at this initial stage, please structure your contributions using the following steps:

Create a directory with your username in the dev dir
Structure your code into one or more Python modules in that directory
- Code should be well-documented. Each function should include a docstring.
Include a Jupyter notebook that demonstrates a run of your code showing printed output, a graph, etc.
- Code cells in the notebook should only call functions defined in your modules. Please do not include any actual code logic in the notebook itself.
- The notebooks should be well-documented. Ideally, each code cell should be preceded by a Markdown cell describing why you have included the code cell. It can also include commments on the output generated, eg. describing features of a graph. These text cells should be more high-level than actual code comments, describing the narrative or thought process behind your contribution.

We request that contributions be structured in this way prior to getting reviewed. If you make subsequent contributions, you can include them in the same directory and reuse module code, but each contribution should include a separate demonstration notebook.

If you wish to build on someone else's contribution, you can import code from their modules into yours. Please do not submit PRs directly modifying code from other contributions at this point, unless to resolve errors or typos.

Issues

Tasks are managed using the GitHub issue tracker.

As issues represent general exploratory tasks at this point, they will generally not be assigned to a single person.
If you want to work on a task, drop a comment in the issue.
You are welcome to make a contribution to an issue even if others are already working on it. You may also expand on someone else's work, eg. testing out the methodology with different datasets or models.
As the project matures, we may start filing targeted issues, eg. to fix specific bugs, which will get assigned to specific person
You are also welcome to contribute your own issues if there is a direction you would like to explore relating to the project focus.

Contributions

Contributions can be made by submitting a pull request against this repository. Learn more about creating a pull request.

We ask each Uplift participant to make a contribution completing #2 (train and test a classification/ regression model). This will help you to become familiar with machine learning and the tools if you are not already. Please submit as a PR following the guidelines above.
- This task must be completed in order to be considered as a participant on this project
If you would like initial feedback on your contribution before it is ready for submission, you may open a PR with "WIP:" at the start of the title and request review. This tag ('work in progress') indicates that the PR is not ready to be merged. When it is ready for final submission, you can modify the title to remove the "WIP:" tag.
Should you use a separate jupyter notebook for comparing different models? If you had a PR merged in to satisfy issue #2 already and are now comparing models for another issue, then a new notebook would be helpful. That being said, a notebook should satisfy the following criteria:

a) it should run beginning to end without error

b) it should be easy to follow and have a clear narrative presenting context, data, results, and interpretation. This may mean some redundancy in code, but will often mean that your notebook is much more helpful to other people looking at it in isolation (including reviewers).

Getting Started

Install Anaconda or Miniconda.
Fork this repository and clone it into your local machine(using git CLI).
Setup and activate environment:

 $ conda env create -f environment.yml
 $ conda activate rdscml

For Windows: Open anaconda prompt and cd into the folder where you cloned the repository

cd RDScML

then type the above commands to activate the environment.

Run Jupyter. The notebook will open in your browser at localhost:8888 by default.

 $ jupyter notebook

After running this commands you will see the notebook containing the datasets and now you can start working with it.

We recommend everyone start by working on #2.

Getting started with GitHub

The git/GitHub open-source workflow can be rather confusing if you haven't used it before. To make a contribution to the project, the general steps you need to take are:

Install git on your computer
Fork the repo on Github (ie. make your own personal copy)
Clone your fork to your local computer
Set remote origin (https://github.com/<user>/RDScML.git) and upstream (https://github.com/archisha-chandel/RDScML.git)
Create a new branch for every issue or new work that you do. (To avoid merge conflicts keep your work in a separate folder in the same branch if it contains more than a few files.)
Commit changes locally on your computer
Push your changes to your fork on Github
Submit a pull request (PR) against the main repo from your fork.

A few commands to start with everytime you work with a GIT repository:

git fetch upstream master
git checkout FETCH_HEAD -b <new_branch_name>
Make changes
git status This will show the files that have been modified, deleted or created
git add . (To add all the modified files) OR git add <file_name> (To add a specific file)
git commit -m '<commit_message>'
git push If you get an error message on executing the above command, enter the suggested git push command.

Now, click on the link that you see once the push command is executed to create a Pull Request. While creating a Pull Request do mention [ Fixes: #<issue_number> ] in the description. This will link the issue to the Pull Request for which the latter is created.

Once your Pull Request is merged do git pull --rebase upstream master. This will update your fork with local changes and the ones made from upstream. This is to ensure there are no file conflicts.

Here are some resources to learn more about parts of this workflow you are unfamiliar with:

GitHub Guides
- In particular, the git handbook explains some of the basics of the version control system
- There is a page explaining the forking/pull request workflow you will be using to contribute.
The Git Book is much more detailed but a good reference
- The Getting Started section is worth reading
- There are also some videos on getting set up
This repo by a previous Outreachy contributor lists many other resources and tutorials.
This video tutorial series on Youtube may also be helpful
First Contributions is a good place to actually practise and put your understanding to test. Feel free to make mistakes as you go along learning to make your first contribution.

Resources

It is highly recommended to study these topics by reading articles on Medium, Towards Data Science, IEEE research papers and other scholarly articles. Popular YouTube channels can also be followed, like

Machine Learning Recipes with Josh Gordon
Natural Language Processing Zero to Hero by Laurence Moroney
Deeplearning.ai
Machine Learning- Andrew Ng, Stanford University (full course)
StatQuest with Josh Starmer Week 1-4 covers the basic prerequisite. This can be skipped if you are not a beginner in this field.

adryanfrois/RDScML