AI-ML Projects for Data Professionals
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Course features
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Author: Manisha Arora
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Cohort: May 12 - Jun 9
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Price: $1,250 USD
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
What's in it for you?
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
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.
Course Curriculum
Class Format
Pre-requisities
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:
Bonus Features:
Who is this course for
Portfolio Building
Data scientists who want to build a compelling portfolio of industry projects to showcase their skills to potential employers.
ML Expertise
Software and data engineers eager to gain expertise in applications of machine learning methodologies to enhance their technical repertoire.
Learn to leverage data-driven insight
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
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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.
This course includes
May 12
Week 1: Learning the Basics
5 LESSONS • 1 PROJECT
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:
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
MAY 13—MAY 19
Week 2: [Case Study 1] Uber ETA Prediction
6 LESSONS • 2 PROJECTS
In this week, we will deep-dive into ML project development and deployment. Goals for this week include:
Putting ML project scoping into action
Intro to deployment using streamlit
Navigating the entire workflow from data exploration to model deployment
Learning coding best practices
MAY 20—MAY 26
Week 3: [Case Study 2] Demand Forecasting
4 LESSONS • 2 PROJECTS
In this week, we will build an ML model to predict energy demand. Goals for this week include:
In this week, we will build an ML model to predict energy demand. Goals for this week include:
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
MAY 27—JUN 2
[Case Study 3] Transformer Based Speech Transcription
4 LESSONS • 3 PROJECTS
In this week, we will get a step further into scoping and building AI projects. Goals for this week include:
Explore various transformer based open-source models
Build an AI project using the an open-source API and pre-trained models
Containerize and deploy the project through Streamlit
Structuring the code for production through modularization, logging and maintainability
JUN 3—JUN 9
Week 5: Build Your Portfolio
This week, we will bring all our learnings together to build a portfolio. Goals for this week include:
1 LESSON • 3 projects
This week, we will bring all our learnings together to build a portfolio. Goals for this week include:
Wrap up on all the projects from past weeks
Build your website and github portfolio
Showcase your work through linkedin posts, blogs and newsletters
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8-10 hours per week
Course Schedule
Live Sessions: Sundays
11:00am - 1:00pm EST
[Optional] Office Hours: Thursdays
8:30pm - 9:00pm EST
Weekly projects
6-8 hours per week
Frequently asked questions
What happens if I can’t make a live session?
I highly recommend you attend the live sessions to encourage active learning. But if you are not able to make it, you will have access to the recordings that you can watch later.
I work full-time, what is the expected time commitment?
The course is designed for working professionals. So most classes would be held during weekday evenings and/or weekends. Refer to the syllabus for details.
This is a hands-on course and each learner is expected to spend 5-10 hours each week to derive the most out of the course.
What’s the refund policy?
All payments made are non-refundable.
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