emmanuel Acheampong

Cohort 1

Cohort 2

Are you ready to launch your AI career but worried about standing out in a crowded field? Go beyond the fundamental knowledge of AI and apply hands-on technical skills to real-world data problems to showcase your skill level. This intensive AI bootcamp equips you with the skills and know-how to build a unique, concise, and game-changing AI portfolio of end-to-end projects showcasing your mastery across three critical domains: 1. Data Analytics2. Computer Vision3. Natural Language Processing. Why 3 domains?Because the future of AI lies in cross-disciplinary expertise. This bootcamp breaks you free from siloed learning, enabling you to: Tackle real-world projects:&nbsp;Develop&nbsp;3 distinct applications&nbsp;that showcase your diverse skillset. Data Analytics Project:&nbsp;Perform exploratory data analysis on a real world project. Computer Vision Project:&nbsp;Build and deploy an image classification model as an API. NLP Project:&nbsp;Build and deploy a Small Language Model as a web application. Skills you'll practice:&nbsp;1. Pytorch2. Scikit Learn3. Pandas4. Flask API5. Heroku6. Google Colab7. Streamlit

Don't Just Learn, Do: The AI Bootcamp Where You Build a Real Portfolio


Course syllabus

Every Friday and Saturday of the cohort session will be a live session where for 2 hours, we will be going through the information and hands-on steps for each project. Each week will be focused on one of the projects and it is essential for students to attend.

Fridays & Saturdays


Course schedule

Intermediate and Advanced Python users trying to dive into AI beyond the theoretical knowledge to practical showcase real skills.

Students interested in building and deploying end-to-end AI projects.

Entry and Mid-level Software engineers looking to pivot into AI through building side projects but need a concrete syllabus to navigate.

Who is this course for

What you’ll get out of this course

Perform exploratory data analysis: A basic concept to understand before diving into ML.


Build UI for ML models: Build a basic Streamlit UI for users to interact with various ML models﻿


Build and train a computer vision model from scratch: An integral part of any computer vision project.


Deploy a computer vision ML model: Turn a model into an API for users to interact with it.


Deploy data analytics and NLP projects as web applications: create real websites where anyone can interact with what you have built.


Build and train a natural language processing(NLP) model from scratch: An integral part for building small language models.


Build an AI portfolio across three critical domains: Data Analytics, Computer Vision, & NLP. Gain real-world AI project experience.

Emmanuel Acheampong

CEO/Head of AI @ roboMUA 


Deploy a computer vision model as an API

Emmanuel Acheampong


Co-Founder/Head of AI @ roboMUA, Notre Dame masters, Morgan Stanley, Microsoft, AI projects course instructor Coursera


Build a python flask API for an already trained Pytorch computer vision model.

Build a python flask API

Deploy flask API to a Heroku account where it can be accessed publicly.

Deploy flask API to the web

Send API calls to Heroku website through curl commands on the command shell and Postman.

Learn directly from emmanuel Acheampong

emmanuel Acheampong

Courses

Advanced AI Portfolio Bootcamp for Software Engineers

Lightning Lessons

Deploy a computer vision model as an API