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.

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ML STRATEGY (1)
0:00:00 Why ML Strategy
0:02:42 Orthogonalization
0:13:21 Single Number Evaluation Metric
0:20:37 Satisficing and Optimization Metric
0:26:35 Train Dev Distributions
0:33:11 Size of the Dev and TEst Sets
0:38:50 When to Change Dev Test Sets and Metrics
0:49:57 Why Human-level Performance
0:55:43 Avoidable Bias
1:02:43 Understanding Human-level Performance
1:13:55 Surpassing Human-level Performance
1:20:17 Improving your Model Performance
1:24:53 Andrej Karpathy Interview

ML STRATEGY (2)
1:40:04 Carrying Out Error Analysis
1:50:36 Cleaning UP Incorrectly Labeled Data
2:03:41 Build your First System Quikly, then iterate
2:09:43 Training and TEsting on Different Distributions
2:20:39 Bias and Variance with Mismatched Data Distributions
2:38:55 Addressing Data Mismatch
2:49:03 Transfer Learning
3:00:21 Multi-task Learning
3:13:20 What is End-to-end Deep Learning
3:25:08 Whether to use End-to-end Deep Learning
3:35:27 Ruslan Salakhutdinov Interview

⭐ Important Notes ⭐
⌨️ This course is created in collaboration with Deeplearning.ai (Andrew NG)

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.

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