Colleagues, the “
Structuring Machine Learning Projects” program is part of the Deep Learning Specialization from DeppLearning.AI. Learn 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 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. Gain highly marketable skills in Decision-Making, Machine Learning, Deep Learning, Inductive Transfer, and Multi-Task Learning. Training modules include: 1) ML Strategy - Orthogonalization, Single Number Evaluation Metric, Satisficing and Optimizing Metric, Train/Dev/Test Distributions, Size of the Dev and Test Sets, When to Change Dev/Test Sets and Metric?, Human-level Performance?, Avoidable Bias, Understanding Human-level Performance, Surpassing Human-level Performance, Improving your Model Performance, and Andrej Karpathy Interview; and 2) ML Strategy - Part II - Carrying Out Error Analysis, Cleaning Up Incorrectly Labeled Data, Build your First System Quickly, then Iterate, Training and Testing on Different Distributions, Bias and Variance with Mismatched Data Distributions, Addressing Data Mismatch, Transfer Learning, Multi-task Learning, What is End-to-end Deep Learning?, Whether to use End-to-end Deep Learning, and Ruslan Salakhutdinov Interview.
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