SMU MBA ML/AI Workshop

1. Install PyCharm
2. Setup a Github account: https://github.com/
3. Download the Github Desktop app for your OS
   • Click on the top left tab that says "Current Repository"
   • Click add and clone the repository: https://github.com/esperie/ml_basics.git
   • Organize all your repositories into a folder
4. Install PyCharm
5. Create New Project in PyCharm and point to the local repository folder
   • Under Python interpreter, select "New Virtual Environment"
6. (Optional) Some settings to consider
   • Settings > Tools > Actions on Save > Check "Reformat Code"
   • Settings > Build, Execution, Deployment > Console > Uncheck "Show Console Variables by Default"
7. Our data is shared in: https://www.dropbox.com/sh/2fju8lxw7fk2hzc/AABulJvzPjnzzk7S2iOHczCra?dl=0
   • Copy the files to your current project folder under store

1. Open the terminal in PyCharm.

2. Install the packages using the following command: pip3 install -U pip pandas colorama tqdm numpy matplotlib scikit-learn scipy plotly pycaret"[full]" ydata-profiling django fastapi"[all]" httpx openpyxl passlib psycopg2-binary pyarrow requests s3fs transformers
3. Go to https://pytorch.org/get-started/locally/ and follow the instructions to install pytorch on your OS (and whether your machine comes with a GPU or not).

1. Install XCode command line tools using the following command: xcode-select --install
2. Install homebrew with: /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
3. Install miniforge with: brew install miniforge
4. Go to settings > Project > Project Interpreter > Click on Add Interpreter > Click Add Local Interpreter > Choose Conda Environment and key in the following in the box: /opt/homebrew/bin/conda
5. Create new environment.
6. Install the packages using the following command: conda install pip pandas colorama tqdm numpy matplotlib scikit-learn scipy plotly ydata-profiling django httpx openpyxl passlib psycopg2-binary pyarrow requests s3fs transformers pycaret
7. Run: pip3 install fastapi"[all]"

1. All functionalities are now consolidated under the applications folder.
2. Each applications folder should have the following structure.

   data: all data etl and wrangling codes

   algo: logic, analytics, ML, DL, AI codes

   models (if created): Django data models

   other django files (if created.)

   views: to serve the data and algo models

3. logs: All log files are kept here.
4. store: All data files are kept here. Model files too.
5. tools: All tools that are not applications specific are kept here.
6. config: All configuration files are kept here.

# All imports on the top

# Setup and global variables section

# Functions section

# Classes section

if __name__ == "__main__':

# execution code

# test code