FRAMEWORK
ACADEMY
Courses
Log In
Join Free
Home
Courses
IT: Artificial Intelligence & Data
Applied Model Training and Evaluation
Curriculum
1 Section
24 Lessons
30 hours
Expand all sections
Collapse all sections
Unit 4 — Applied Model Training and Evaluation
24
1.1
Considering AI and Machine Learning Specifications
20 mins
1.2
Defining What Training Means
20 mins
1.3
Using Hardware Acceleration
20 mins
1.4
Downloading the Datasets and Code
20 mins
1.5
Avoiding Variance of Estimates and Leakage Traps
20 mins
1.6
Learning One Example at a Time
20 mins
1.7
Testing Multiple Models
20 mins
1.8
Shunning Discriminatory Practices
20 mins
1.9
Defining the Divide Between Art, Science, and Engineering
20 mins
1.10
Viewing Your Notebook
20 mins
1.11
Applying Feature Engineering
20 mins
1.12
Detecting Black Swans in Code
20 mins
1.13
Predicting the Next AI Winter
20 mins
1.14
Executing the Code
20 mins
1.15
Selecting Features
20 mins
1.16
Understanding the Process
20 mins
1.17
Sharing Your Notebook
20 mins
1.18
Looking for More Data
20 mins
1.19
Considering the Consequences
20 mins
1.20
Getting Help
20 mins
1.21
Blending Models
20 mins
1.22
Balancing Decision-Making
20 mins
1.23
Stacking Models
20 mins
1.24
Verifying a Data Source
20 mins
This content is protected, please
login
and
enroll
in the course to view this content!
S
Ask SIERA
YOUR DIGITAL ASSISTANT
×
Modal title
Main Content