Duke University - Machine Learning Foundations for Product Managers
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Machine Learning Foundations for Product Managers at Coursera Overview
Duration | 16 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Machine Learning Foundations for Product Managers at Coursera Highlights
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 3 in the AI Product Management Specialization
Machine Learning Foundations for Product Managers at Coursera Course details
- In this first course of the AI Product Management Specialization offered by Duke University's Pratt School of Engineering, you will build a foundational understanding of what machine learning is, how it works and when and why it is applied. To successfully manage an AI team or product and work collaboratively with data scientists, software engineers, and customers you need to understand the basics of machine learning technology. This course provides a non-coding introduction to machine learning, with focus on the process of developing models, ML model evaluation and interpretation, and the intuition behind common ML and deep learning algorithms. The course will conclude with a hands-on project in which you will have a chance to train and optimize a machine learning model on a simple real-world problem.
- At the conclusion of this course, you should be able to:
- 1) Explain how machine learning works and the types of machine learning
- 2) Describe the challenges of modeling and strategies to overcome them
- 3) Identify the primary algorithms used for common ML tasks and their use cases
- 4) Explain deep learning and its strengths and challenges relative to other forms of machine learning
- 5) Implement best practices in evaluating and interpreting ML models
Machine Learning Foundations for Product Managers at Coursera Curriculum
What is Machine Learning
Specialization Overview
Instructor Introduction
Course Overiew
Module 1 Introduction & Objectives
Introduction to Machine Learning
Data Terminology
What is a Model?
Types of Machine Learning
What ML Can and Cannot Do
Module Wrap-up
About the Course
Module 1 Slides
Module 1 Quiz
The Modeling Process
Introduction and Objectives
Building a Model
Feature Selection
Algorithm Selection
Bias-Variance Tradeoff
Test and Validation Sets
Cross Validation
Module Wrap-up
Download Module Slides
Module 2 Quiz
Evaluating & Interpreting Models
Introduction and Objectives
Outcomes vs Outputs
Model Output Metrics
Regression Error Metrics
Classification Error Metrics: Confusion Matrix
Classification Error Metrics: ROC and PR Curves
Troubleshooting Model Performance
Module Wrap-up
Download Module Slides
Module 3 Quiz
Linear Models
Introduction and Objectives
Linear Regression
Regularization
Logistic Regression
Softmax Regression
Module Wrap-up
Download Module Slides
Module 4 Quiz
Trees, Ensemble Models and Clustering
Introduction and Objectives
Tree Models
Ensemble Models
Random Forest
Clustering
K-Means Clustering
Module Wrap-up
Download Module Slides
Module 5 Quiz
Deep Learning & Course Project
Introduction and Objectives
Introduction to Deep Learning
Artificial Neurons
From Neurons to Neural Networks
Training Neural Networks
Computer Vision
Natural Language Processing
Module Wrap-up
Course Wrap-up
Download Module Slides
Course Project Modeling Options
Module 6 Quiz
Machine Learning Foundations for Product Managers at Coursera Admission Process
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