Structuring Machine Learning Projects
About this ntirawen: You will learn how to build a successful machine
learning project. If you aspire to be a technical leader in AI, and know
how to set direction for your team's work, this course will show you
how. Much of this content has never been taught elsewhere, and is drawn
from my experience building and shipping many deep learning products.
This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience.
You will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this post. I hope this two ntirawen course will save you months of time.
This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization.
Who is this class for: Pre-requisites: - This ntirawen is aimed at individuals with basic knowledge of machine learning, who want to know how to set technical direction and prioritization for their work. - It is recommended that you take course one and two of this specialization (Neural Networks and Deep Learning, and Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization) prior to beginning this course.
ML Strategy
Learning Objectives
• Understand 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
• Understand how to define human-level performance
• Use human-level perform to define your key priorities in ML projects
This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience.
You will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this post. I hope this two ntirawen course will save you months of time.
This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization.
Who is this class for: Pre-requisites: - This ntirawen is aimed at individuals with basic knowledge of machine learning, who want to know how to set technical direction and prioritization for their work. - It is recommended that you take course one and two of this specialization (Neural Networks and Deep Learning, and Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization) prior to beginning this course.
ML Strategy
Learning Objectives
• Understand 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
• Understand how to define human-level performance
• Use human-level perform to define your key priorities in ML projects
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