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[ FreeCourseWeb com ] Oreilly - Managing Machine Learning Projects, Video Edition
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001. Chapter 1. Introduction Delivering machine learning projects is hard; let’s do it better.mp4
002. Chapter 1. Why is ML important.mp4
003. Chapter 1. Other machine learning methodologies.mp4
004. Chapter 1. Understanding this book.mp4
005. Chapter 1. Case study The Bike Shop.mp4
006. Chapter 1. Summary.mp4
007. Chapter 2. Pre-project From opportunity to requirements.mp4
008. Chapter 2. Project management infrastructure.mp4
009. Chapter 2. Project requirements.mp4
010. Chapter 2. Data.mp4
011. Chapter 2. Security and privacy.mp4
012. Chapter 2. Corporate responsibility, regulation, and ethical considerations.mp4
013. Chapter 2. Development architecture and process.mp4
014. Chapter 2. Summary.mp4
015. Chapter 3. Pre-project From requirements to proposal.mp4
016. Chapter 3. Create an estimate.mp4
017. Chapter 3. Pre-salespre-project administration.mp4
018. Chapter 3. Pre-projectpre-sales checklist.mp4
019. Chapter 3. The Bike Shop pre-sales.mp4
020. Chapter 3. Pre-project postscript.mp4
021. Chapter 3. Summary.mp4
022. Chapter 4. Getting started.mp4
023. Chapter 4. Finalize team design and resourcing.mp4
025. Chapter 4. Infrastructure plan.mp4
026. Chapter 4. The data story.mp4
027. Chapter 4. Privacy, security, and an ethics plan.mp4
028. Chapter 4. Project roadmap.mp4
029. Chapter 4. Sprint 0 checklist.mp4
030. Chapter 4. Bike Shop project setup.mp4
031. Chapter 4. Summary.mp4
032. Chapter 5. Diving into the problem.mp4
033. Chapter 5. Understanding the data.mp4
034. Chapter 5. Business problem refinement, UX, and application design.mp4
035. Chapter 5. Building data pipelines.mp4
036. Chapter 5. Model repository and model versioning.mp4
037. Chapter 5. Summary.mp4
038. Chapter 6. EDA, ethics, and baseline evaluations.mp4
039. Chapter 6. Ethics checkpoint.mp4
040. Chapter 6. Baseline models and performance.mp4
041. Chapter 6. What if there are problems.mp4
042. Chapter 6. Pre-modeling checklist.mp4
043. Chapter 6. The Bike Shop Pre-modelling.mp4
045. Chapter 7. Making useful models with ML.mp4
046. Chapter 7. Feature engineering and data augmentation.mp4
048. Chapter 7. Making models with ML.mp4
049. Chapter 7. Stinky, dirty, no good, smelly models.mp4
050. Chapter 7. Summary.mp4
051. Chapter 8. Testing and selection.mp4
052. Chapter 8. Testing processes.mp4
053. Chapter 8. Model selection.mp4
054. Chapter 8. Post modelling checklist.mp4
055. Chapter 8. The Bike Shop sprint 2.mp4
056. Chapter 8. Summary.mp4
057. Chapter 9. Sprint 3 system building and production.mp4
058. Chapter 9. Types of ML implementations.mp4
059. Chapter 9. Nonfunctional review.mp4
060. Chapter 9. Implementing the production system.mp4
061. Chapter 9. Logging, monitoring, management, feedback, and documentation.mp4
062. Chapter 9. Pre-release testing.mp4
063. Chapter 9. Ethics review.mp4
064. Chapter 9. Promotion to production.mp4
065. Chapter 9. You aren’t done yet.mp4
066. Chapter 9. The Bike Shop sprint 3.mp4
067. Chapter 9. Summary.mp4
068. Chapter 10. Post project (sprint ?).mp4
069. Chapter 10. Off your hands and into production.mp4
070. Chapter 10. Team post-project review.mp4
071. Chapter 10. Improving practice.mp4
072. Chapter 10. New technology adoption.mp4
073. Chapter 10. Case study.mp4
074. Chapter 10. Goodbye and good luck.mp4
075. Chapter 10. Summary.mp4
Bonus Resources.txt
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