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.