# 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.