Cohort 2

Gain hands-on experience and build a portfolio of industry AI/ML projects. Scope & execute the workflow from data exploration to deployment.

Manisha Arora

Data Science Lead, Google | Founder, PrepVector | 
MIT, UT Austin & Univ of Cincinnati

Siddarth Ranganathan

Director of Data Science, Microsoft | Founder, PrepVector | USC Marshall

AI-ML Projects for Data Professionals

Google

Axtria

Massachusetts Institute of Technology

UT Austin

University of Cincinnati

If you want to succeed as a Data Scientist in tech, proficiency in 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 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 effectively2) Lead discussions with stakeholders to align on project objectives and get buy-in3) Navigate the entire data science workflow from data exploration to model deployment4) Communicate project insights and business impact to stakeholders Course Curriculum:Week 1: Scoping ML Projects, Stakeholder Buy-In Strategies, and Data Science Workflow on GitWeek 2: Data Cleaning, Feature Engineering, ML algorithms, Deployment on StreamlitWeek 3: Ensemble Models, Forecasting Methods, ML Tradeoffs, Actionable Insights, Coding Best PracticesWeek 4: Model Deployment on Cloud, Coding Best Practices, Github Portfolio ShowcaseWeek 5: Project Discussion, Set up Portfolio, Build your Website, Showcase your Work Pre-requisites:1. Familiarity with R / Python programming language2. Knowledge of data manipulation using Pandas3. Understanding of machine learning fundamentals is good-to-have4. 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&amp;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!

Become a Data Science Expert by Implementing End-to-End AI/ML Projects

Data scientists who want to build a compelling portfolio of industry projects to showcase their skills to potential employers.

Software and data engineers eager to gain expertise in applications of machine learning methodologies to enhance their technical repertoire.

Data and BI analysts seeking to acquire hands-on experience in leveraging data-driven insights to solve industry challenges.

Who is this course for

What you’ll get out of this course

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.

Master Practical Applications of Machine Learning Methodologies

Develop a structured approach to navigating complexities of scoping ML projects, understanding business problems, gathering requirements, implementing projects, and deploying them on Cloud.

Implement End-to-End Data Science Projects & Deploy on Cloud

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.

Build a Compelling Portfolio of Industry Projects using Organized Workflows on GitHub

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.

Drive Actionable Insights for Informed Decision-Making

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.

Enhance Cross-Functional Collaboration and Communication

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

What people are saying

Meet your instructor

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.

Live Sessions: Sundays

Optional office hours once a week to answer any questions as you digest the content and work on your project.

[Optional] Office Hours: Thursdays

Take time to work on the project as per the instructions provided for each week. These projects will be discussed during our live sessions.

Weekly projects

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

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About this course

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