AI/ML Projects Course
Gain hands-on experience and build a portfolio of industry AI/ML projects. Scope & execute the workflow from data exploration to deployment.
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 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 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
Who this Course is for?
a) Data scientists who want to build a compelling portfolio of industry projects to showcase their skills to potential employers.
b) Software and data engineers eager to gain expertise in applications of machine learning methodologies to enhance their technical repertoire.
c) Data and BI analysts seeking to acquire hands-on experience in leveraging data-driven insights to solve industry challenges.
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: 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!
I look forward to seeing you in the course. If you have any questions, feel free to book 1:1 time with me, and I’ll be happy to address any questions.