AI/ML for Non Data Scientists
summary
Claim: data scientists spend ~80% of their time cleaning and preparing data, so non‑coders can contribute by identifying business cases (churn, recommendations, DNS anomaly detection), doing data visualization in Excel, or supplying domain answers for missing values. Tools named: HDFS, MapReduce, Spark for distributed runs that cut processing from days to hours or minutes. Typical modeling can be done in ~20–30 lines of code, expanding features from ~25 to ~279 then pruning to ~38 for feature selection and data engineering.
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chapters (10)
- 0:00Introduction to AI/ML
- 1:04Defining AI ML and Data Science
- 2:30Why people avoid AI/ML
- 5:27Understanding business cases
- 9:22Working with data insights
- 13:42Data cleaning and analysis
- 15:49Data engineering role
- 17:11Modeling and prediction
- 23:54Building AI/ML expertise
- 25:23Summary and next steps
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