Welcome to the ML-Engineer Workbook! This repository is a comprehensive, structured, and engaging roadmap designed to guide you on your journey to becoming a Machine Learning Engineer.
This is a self-paced workbook crafted for aspiring Machine Learning Engineers. It covers essential concepts, tools, and skills, with hands-on examples, real-world projects, and additional resources to deepen your knowledge and help you build a professional portfolio.
The workbook is organized into nine major sections, numbered for easy progression. Here’s a quick rundown of what each section covers:
- Getting Started: Lay the foundation with Python fundamentals and machine learning basics.
- Data Preprocessing: Clean, wrangle, and engineer your data for analysis.
- EDA (Exploratory Data Analysis): Gain insights through visualization, statistics, and correlation analysis.
- Algorithms and Models: Master supervised and unsupervised learning, deep learning, and ensemble methods.
- Model Evaluation: Evaluate model performance using metrics and cross-validation techniques.
- Deployment and Scaling: Learn how to deploy models using Docker, FastAPI, and explore MLOps principles.
- Advanced Topics: Delve into NLP, computer vision, and reinforcement learning.
- Resources: A collection of books, courses, and papers to expand your learning.
- Projects: Apply your knowledge with hands-on projects, which can be added to your portfolio.
- Start from Section 1 and progress sequentially. Each folder contains explanatory notebooks, code samples, and practice tasks.
- Complete project assignments in the Projects folder to reinforce your learning.
- Refer to the Resources section for books, courses, and research papers that complement each topic.
- Use the
README.mdfiles within each folder as a guide for completing the tasks.
- Languages: Python
- Libraries: NumPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow/PyTorch (for deep learning sections)
- Deployment: Docker, FastAPI
- MLOps: GitHub Actions (for CI/CD), MLflow
- Data Visualization: Seaborn, Plotly
- Clone the Repository:
git clone https://github.com/SaiKapilKumar/ML-Engineer.git