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AI-ML Projects for Data Professionals
Gain hands-on experience and build a portfolio of industry AI/ML projects. Scope & execute the workflow from data exploration to deployment.
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Course overview
Become a Data Science Expert by Implementing End-to-End AI/ML Projects
If you want to succeed as a Data Scientist in tech, proficiency in AI / ML concepts is just the beginning. To truly thrive, you must implement end-to-end projects, integrate business acumen, and effectively collaborate with stakeholders. This advanced course is designed to equip mid-senior career professionals to drive impact while building a portfolio of applied AI & ML projects.
This course offers a dynamic blend of technical expertise and real-world business challenges. Through a series of interactive sessions, discussions, and hands-on projects, you will learn how to:
1) Scope machine learning projects effectively
2) Lead discussions with stakeholders to align on project objectives and get buy-in
3) Navigate the entire data science workflow from data exploration to model deployment
4) Communicate project insights and business impact to stakeholders
Course Curriculum:
Week 1: Scoping ML Projects, Stakeholder Buy-In Strategies, and Data Science Workflow on Git
Week 2: Data Cleaning, Feature Engineering, ML algorithms, Deployment on Streamlit
Week 3: Agentic AI and Frameworks, Ensemble Models, Forecasting Methods, ML Tradeoffs, Actionable Insights, Coding Best Practices
Week 4: Model Deployment on Cloud, Coding Best Practices, Github Portfolio Showcase
Week 5: Project Discussion, Set up Portfolio, Build your Website, Showcase your Work
Pre-requisites:
1. Familiarity with R / Python programming language
2. Knowledge of data manipulation using Pandas
3. Understanding of machine learning fundamentals is good-to-have
4. Learning curiosity 🙂
Time-commitment:
8-10 hours per week
Class Format:
Each 2-hour session will feature 1.5 hours of content-rich instruction followed by 30 minutes of open discussion and Q&A. Prior to each class, you will be expected to engage in pre-readings, hands-on exercises, and GitHub submissions. During sessions, we'll explore various problem-solving techniques, address nuances and trade-offs, and derive actionable insights to drive business objectives forward.
Bonus Features:
In addition to course content, you will have the opportunity to showcase your best projects weekly on the PrepVector Newsletter and LinkedIn page. Outstanding projects will also receive special recognition on LinkedIn by me!
Who is this course for
01
Data scientists who want to build a compelling portfolio of industry projects to showcase their skills to potential employers.
02
Software and data engineers eager to gain expertise in applications of machine learning methodologies to enhance their technical repertoire.
03
Data and BI analysts seeking to acquire hands-on experience in leveraging data-driven insights to solve industry challenges.
What you’ll get out of this course
Master Practical Applications of Machine Learning Methodologies
Gain hands-on experience in applying machine learning methodologies to real-world industry scenarios. Learn how to process data effectively, select the right algorithms, and implement models for accuracy and efficiency.
Implement End-to-End Data Science Projects & Deploy on Cloud
Develop a structured approach to navigating complexities of scoping ML projects, understanding business problems, gathering requirements, implementing projects, and deploying them on Cloud.
Build a Compelling Portfolio of Industry Projects using Organized Workflows on GitHub
Construct a portfolio of industry projects using engineering best practices, that showcase your ability to develop projects through organized workflows and coding best practices on GitHub.
Drive Actionable Insights for Informed Decision-Making
Extract meaningful insights from data and translate them into actionable recommendations for decision-makers. Explore techniques for visualizing and communicating data-driven insights, enabling informed decision-making and driving business growth.
Enhance Cross-Functional Collaboration and Communication
Collaborate effectively with diverse stakeholders, including technical and non-technical members. Hone your communication skills to frame narratives, convey complex technical concepts in a compelling and impactful manner, fostering collaboration and alignment.
What’s included
Live sessions
Learn directly from Manisha Arora & Siddarth Ranganathan in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
19 live sessions • 20 lessons • 10 projects
Week 1
Revisit fundamentals of machine learning
Learn Git for version control and collaborative development
Understand Data Science Workflow on Github
Set up Github repository where we will host our projects
- ⚙️
Welcome!
- ⚙️
Module Overview
- 📄
Data Science Foundations
- 📄
Introduction to Git
- 📄
Organizing a Data Science Workflow
- ✍️
Create Your Github Repository
Submit by Feb 3
Jan
25
Machine Learning Foundations
Jan
25
DS Workflow & Github Setup
Jan
25
Case Study 1: Problem Walkthrough
Week 1: Learning the Basics
In this week, we will gain a comprehensive understanding of fundamentals of data science and set up the basics for project deployment in the consecutive weeks. Here are the goals for this week:
Week 2
Putting ML project scoping into action
Containerizing ML applications using Docker
Intro to deployment using Streamlit
Navigating the entire workflow from data exploration to model deployment
Learning coding best practices
- ⚙️
Module Overview
- 📄
Scoping Machine Learning Projects
- 📄
Intro to Containerization
- 📄
Intro to StreamLit Deployment
- 📄
Problem Statement
- 📄
Data & Dictionary
- 📄
Coding Best Practices
- ✍️
Build Your Code: Uber ETA Prediction
Submit by Feb 10 - ✍️
[Optional] Showcase Your Work!
Submit by Feb 10
Jan
30
[Optional] Office Hour
Feb
1
[Case Study 1] Uber ETA Prediction Problem Scoping
Feb
1
[Case Study 1] Uber ETA Prediction Code Review
Feb
1
[Case Study 1] Model Deployment in Streamlit
Feb
1
Case Study 2: Problem Walkthrough
Week 2: [Case Study 1] Uber ETA Prediction
In this week, we will deep-dive into ML project development and deployment. Goals for this week include:
Week 3
Scope out the demand forecasting project
Explore various forecasting methodologies like Holt Winter and Prophet
Build out the demand forecasting code and deploy it using streamlit
- ⚙️
Module Overview
Complete by Feb 16 - 📄
Explore Forecasting Methodologies
- 📄
Problem Statement
- 📄
Data & Data Description
- ✍️
Build Your Code: Demand Forecasting
Submit by Mar 3 - ✍️
[Optional] Showcase Your Work!
Submit by Mar 3
Feb
6
[Optional] Office Hour
Feb
8
[Case Study 2] Demand Forecasting Problem Scoping
Feb
8
[Case Study 2] Demand Forecasting Code Review
Feb
8
Case Study 3: Problem Walkthrough
Week 3: [Case Study 2] Demand Forecasting
In this week, we will build an ML model to predict energy demand. Goals for this week include:
Week 4
Learn to design and build autonomous AI agents.
Develop effective prompt engineering for agent workflows.
Deploy and run LLMs locally and through cloud APIs.
Execute a real-world Agentic AI project end-to-end.
- ⚙️
Module Overview
- 📄
Pre-reads
- 📄
Problem Statement
- ✍️
Build Your Code:
Submit by Dec 31 - ✍️
[Optional] Showcase Your Work!
Submit by Dec 31
Feb
13
[Optional] Office Hour
Feb
15
[Case Study 3] Speech-to-Text Problem Scoping
Feb
15
[Case Study 3] Speech to Text Code Review
[Case Study 3] Automating Insight Generation Using Agentic AI
In this week, we will get a step further into scoping and building AI projects. Goals for this week include:
Week 5
Wrap up on all the projects from past weeks
Build your website and github portfolio
Showcase your work through linkedin posts, blogs and newsletters
- ⚙️
Module Overview
- ✍️
Build Your Github Repo
Submit by Mar 3 - ✍️
Set Up Your Website
Submit by Mar 3 - ✍️
Showcase Your Work!
Submit by Mar 3
Feb
20
[Optional] Office Hour
Week 5: Build Your Portfolio
In this week, we will bring all our learnings together to build a portfolio. Goals for this week include:
Feb
22
Github Portfolio Review
Feb
22
Website Setup
Feb
22
Showcase Your Work
What people are saying
Abhigna Pebbati
Ketki Sharma
Vedhanarayan Ravi
Indu Seetharaman
Meet your instructor
Manisha Arora
Manisha Arora
Data Science Lead, Google | Founder, PrepVector | MIT, UT Austin & Univ of Cincinnati
I am a seasoned Data Science professional with 10+ years of experience leading data science teams and driving business growth through data-driven decision making.
I am passionate about democratizing data science and enabling others level up in their careers. I found PrepVector to enable aspiring professionals to excel in there data science careers. I have taught 350+ data professionals through my courses at Maven & PrepVector.
Siddarth Ranganathan
Siddarth Ranganathan
Director of Data Science, Microsoft | Founder, PrepVector | USC Marshall
I am a data and product analytics professional with 20 years of experience across Tech, eCommerce, Healthcare, and Supply Chain industries.
Currently, I serve as the Director of Data Science at Microsoft Azure, where I lead a team of 40+ data scientists and engineers to drive high-impact initiatives that empower Azure's growth and innovation.
Course schedule
8-10 hours per week
Live Sessions: Sundays
11:00am - 1:00pm EST
We will meet every Sunday to discuss the weekly updates and review codes. You will get to work together with other learners in the course and learn from them.
[Optional] Office Hours: Thursdays
8:30pm - 9:00pm EST
Optional office hours once a week to answer any questions as you digest the content and work on your project.
Weekly projects
6-8 hours per week
Take time to work on the project as per the instructions provided for each week. These projects will be discussed during our live sessions.
Learning is better with cohorts
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you