Many thanks to Stanford and MIT for sharing knowledge. In 2000 I used to download PPTs and refer to slides. Now you have a ton of materials plus a lot of distraction. Filtering knowledge vs chaos need time, focus and bookmarking.
Course materials are shared in Link
I wanted to review the first lecture/notes and bookmark my lessons
Notes - Link
Summary from it
Machine learning is an approach to (1) learn (2) complex (3) patterns from (4) existing data and use these patterns to make (5) predictions on (6) unseen data.
- Learn: DB has explicit relationships but ML learns relationships
- Complex: Across attributes ML finds relationships
- Patterns: Influences, categories, segments ML finds
- Existing data: Learn from data, improve on ongoing data collection.
- Predictions: Use the learnt knowledge to apply for incoming data
Use cases distributions, Top 3 outside costs
- Customer insights
- Improve experience
- Retain Customers
Data Hierarchy - Data - OLTP - OLAP - ML - AI
Difference between Latency and throughput
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