C3 - Structuring Machine Learning Projects

https://www.coursera.org/learn/machine-learning-projects

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.

By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.

This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the “industry experience” that you might otherwise get only after years of ML work experience.

The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

SKILLS YOU WILL GAIN

• Deep Learning
• Inductive Transfer
• Machine Learning
• Multi-Task Learning
• Decision-Making

Week 1 - ML strategy

Streamline and optimize your ML production workflow by implementing strategic guidelines for goal-setting and applying human-level performance to help define key priorities.

Learning Objectives

• Explain why Machine Learning strategy is important
• Apply satisficing and optimizing metrics to set up your goal for ML projects
• Choose a correct train/dev/test split of your dataset
• Define human-level performance
• Use human-level performance to define key priorities in ML projects
• Take the correct ML Strategic decision based on observations of performances and dataset

Week 2 - ML strategy

Develop time-saving error analysis procedures to evaluate the most worthwhile options to pursue and gain intuition for how to split your data and when to use multi-task, transfer, and end-to-end deep learning.

Learning Objectives

• Describe multi-task learning and transfer learning
• Recognize bias, variance and data-mismatch by looking at the performances of your algorithm on train/dev/test sets