UPenn - AI Fundamentals for Non-Data Scientists
- Offered byCoursera
AI Fundamentals for Non-Data Scientists at Coursera Overview
Duration | 7 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
AI Fundamentals for Non-Data Scientists at Coursera Highlights
- Earn a Certificate upon completion
- Course 1 of 4 in the AI For Business Specialization
- Financial aid available
AI Fundamentals for Non-Data Scientists at Coursera Course details
- In-depth to discover how Machine Learning is used to handle and interpret Big Data
- Get a detailed look at the various ways and methods to create algorithms to incorporate into your business with such tools as Teachable Machine and TensorFlow
- Learn different ML methods, Deep Learning, as well as the limitations but also how to drive accuracy and use the best training data for your algorithms
- Explore GANs and VAEs, using your newfound knowledge to engage with AutoML to help you start building algorithms that work to suit your needs
- Learned different ways to code, including how to use no-code tools, understand Deep Learning, how to measure and review errors in your algorithms, and how to use Big Data to not only maintain customer privacy but also how to use this data to develop different strategies that will drive your business
AI Fundamentals for Non-Data Scientists at Coursera Curriculum
Module 1 -Big Data and Artificial Intelligence
AI for Business Introduction
Course Introduction
Big Data Overview
Big Data Analysis
Data Management Tools
Data Management Infrastructure
Data Analysis: Extracting Intelligence from Big Data
Introduction to Artificial Intelligence
Machine Learning Overview
Reinforcement Learning
A Detailed View of Machine Learning
Module 1 Slides
Module 1 Quiz
Module 2 -Training and Evaluating Machine Learning Algorithms
Specific Machine Learning Methods: A Deep Dive
Intro to Model Selection
Feature Engineering and Deep Learning Introduction
Deep Learning
How Deep Learning Works
Limitations of Deep Learning
Evaluating ML Performance
Common Loss Functions
Tradeoffs Between Loss Functions
How is Training Data Acquired
The Over-Fitting Problem
Test Data
Examples of End-to End Work Flow
Module 2 Slides
Module 2 Quiz
Module 3 -GANs and VAEs
Natural Language Processing
GANs and VAEs
Intro to AutoML
Using AutoML
Teachable Machine
TensorFlow Playground
ML Operations
Chicken and the Egg
Module 3 Slides
Module 3 Quiz
Module 4 - Industry Interviews
Interview With Ed Lee
AI Fundamentals for Non-Data Scientists at Coursera Admission Process
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