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Bootcamp - ML Engineering 101
13 Week of 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.
Last modified 8mo ago