Book Review - Building Machine Learning Powered Applications by Emmanuel Ameisen
I found this book a bit frustrating to follow.
Wherever the code is shown in the book and a link provided - it is to the main github repo and not to the individual jupyter notebook or python code. Nor was the filename mentioned beneath the code snippet.
Not a deal breaker but it is frustrating.
Pros
- Covers a lot of theory on building a machine learning application
- You build a ML Editor that gives recommendations
- Important concepts are illustrated in specific notebooks
- Incremental changes to the model for better results
Cons
- More theory, less code
- Light on deployment details
- Book links point only to the main repo and not the specific file with the code
- Not all tied together (filter models / tests).
My github repo - It contains the chapter numbers in the filenames.
Some images from the final built machine learning app with recommendations.
1. Main page of ML Editor
2. Model V1 - Sample Question
3. Model V1 - Readability score
4. Model V2 - Sample Question
5. Model V2 - Question Score
6. Model V3 - Sample Question
7. Model V3 - Detailed Recommendations
8. Model V3 - Another Recommendation example (Copied from writers.stackexchange.com)
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