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ProductizeML
Introduction
Objectives
About the Course
Guidelines
Syllabus
After Completion
Machine Learning
Why ML, and why now
Supervised Learning
Unsupervised Learning
Deep Learning
ML Terminology
Data Management
Data Access
Data Collection
Data Curation
Train and Evaluate
Framework and Hardware
Training Neural Networks
Model Evaluation
Productize It
ML Life Cycle
Business Objectives
Data Preparation
Model Development
Train, Evaluate, and Deploy
A/B Testing
KPI Evaluation
PM Terminology
Resources
Readings
Courses
Videos
Hands-On
Python for Machine Learning
Powered By
GitBook
After Completion
In general terms, after completing this course participants will be more familiar and confident with:
Common and most-used
AI
,
Machine Learning
,
Deep Learning
, and
Data Science
terminology.
What someone can expect
realistically
from these systems to do and what they actually cannot do.
Abilities to understand what the
opportunities
of an
AI/ML product
are, and how these can impact their institution, organization, or personal project.
Identify and prioritize the
highest value applications
for machine learning and do what it takes to make them successful.
Computational resources
and recommended
frameworks.
Best practices on
data collection
and
curation.
Training, optimization, and monitoring of Machine Learning models.
Instructions on how to build reproducible
machine learning pipelines.
Create continuous and automated integration to
deploy models.
When building a product, what things we want the AI solution to improve or enhance when compared to traditional solutions.
Recommendations on how to
deliver AI/ML-generated predictions
to users.
Understand and practice with
real-world cases
and
users' data
.
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Syllabus
Next - Machine Learning
Why ML, and why now
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1yr ago
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