# Bootcamp - ML Engineering 101 ## Python with ML Basics Bootcamp: ### Objectives: TBD ### Topics: **Python Basics:** * TBD **Python Advanced:** * Class * Design Patterns * Error Handling * Errors with Enums * Load config files **ML Basics:** * Numpy * Scipy * Pandas * Scikit Learn * Matplotlib * Seaborn * Text Analysis * Visualization * TBD **ML Tools/ Libraries Mining:** * AllenNLP * TextBlob * Spacy * NLTK * TBD **ML Advanced:** * Feature Engineering * Feature Abstraction * Normalization * Smalltics * **Flask Basics:** * Simple server * Port change * TBD **Flask Advanced:** * Flask-Script Manager * Gunicorn with Manager **Docker Basics:** * Run with Docker compose * TBD **FastAPI:** * TBD **Git Basics:** * GitHub * GitLab * Git Branch * Pull Request * Review pull request **Git Advanced:** * Git submodule * Git ignore with advanced configurations * Git LFS * Git history deletion **Agile:** * Basic Agile concepts * Scrum Poker * TBD **Heroku:** * TBD **Database:** * SQLite * PostgreSQL * MySQL * MongoDB * MS-SQL **Research Work:** * TBD ## Duration: **Total Hours:** \ 520 Hours \ \ This is including your assignment and other factors **Mentoring:** \ 260 Hours **Assignment, clarification with mentors, Assignment validation:** \ 260 Hours ## Featurepreneur Uniqueness * Students teach students * Flexible timing (can be extended to 17-20 weeks) * Gamified teaching methods * 50% tech, 50% games * Natural learning with Memes * Get the hands dirty on the Day 1 * Zero slides, Zero theories * Experts share their experience from the industrial point of view * Real time scenarios with errors * Focus more on the error scenarios and problem solving oriented * AWS access to students (1-2 students per season) * Capstone projects * Industrial strength coaching with complex code base ## Graduation Criteria * You should finish at least 50 hours of volunteer work with any ML Researchers (non-indian preferred) * You should have done a minimum of 2 Capstone projects * You should have created one open source projects or contributed 20 hours on any existing open source projects * You should have shown a demo for 25+ audience * You should have done at least 100 hours assignment * You should have done at least 2 Random Quick Projects with TactLabs ## Benefits: * You might be eligible to get **AWS ML Exam scholarship** from Featurepreneur fund (once in every 5 months) * Your feature might be eligible for **Tact Coins**. Coin Assessment Board (**CAB**) will have to decide whether your feature is eligible for Tact Coins or not. They will do the assessment and let you know if yes and how much. * Some gaming features might be eligible for Royalty payment (per run or per month). This assessment has to be done by the **CAB** as well. * Based on your learning and commitment, you might be eligible to get into Internship with TactLabs for one of these titles: DevOps Intern, MLOps Intern, Research Intern, Developer Intern, & SWAT Intern. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://tactlabs.gitbook.io/featurepreneur/bootcamp-ml-engineering-101.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.