Cohort 1

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 


Advanced AI Portfolio Bootcamp for Software Engineers

Morgan Stanley

Microsoft

Coursera

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


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.


Provide a brief description for each module as a way to sell the value of what students will be learning throughout the course.

First Module

Second Module

Third Module

Course syllabus

Meet your instructor

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

This course builds on live workshops and hands-on projects

Active hands-on learning

You’ll be interacting with other learners through breakout rooms and project teams

Interactive and project-based

Join a community of like-minded people who want to learn and grow alongside you

Course completed by

About this course

Get notified about the next cohort

Learn from live sessions