This course series by Andrew Ng provides practical guidance on how to structure successful machine learning projects. Learn key steps like defining objectives, managing data, and selecting appropriate models. Each video covers essential aspects of workflow, from model training to evaluation and deployment. Perfect for building robust, scalable machine learning solutions.
Improving Model Performance (C3W1L01)
Orthogonalization (C3W1L02 )
Single Number Evaluation Metric (C3W1L03)
Satisficing and Optimizing Metrics (C3W1L04)
Train/Dev/Test Set Distributions (C3W1L05)
Sizeof Dev and Test Sets (C3W1L06)
When to Change Dev/Test Sets (C3W1L07)
C3W1L08 WhyHumanLevelPerformance
Understanding Human-Level Performance? (C3W1L10)
Surpassing Human-Level Performance (C3W1L11)
Improving Model Performance (C3W1L12)
Carrying Out Error Analysis (C3W2L01)
Cleaning Up Incorrectly Labelled Data (C3W2L02)
Build First System Quickly, Then Iterate (C3W2L03)
Training and Testing on Different Distributions (C3W2L04)
Bias and Variance With Mismatched Data (C3W2L05)
Addressing Data Mismatch (C3W2L06)
Transfer Learning (C3W2L07)
Multitask Learning (C3W2L08)
What is end-to-end deep learning? (C3W2L09)
Whether to Use End-To-End Deep Learning (C3W2L10)
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