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[ FreeCourseWeb com ] Mastering Large Datasets with Python, Video Edition

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Name:[ FreeCourseWeb com ] Mastering Large Datasets with Python, Video Edition

Infohash: BE478E7E364ED7F705B50E1E0594437E1C10C665

Total Size: 1.05 GB

Seeds: 4

Leechers: 3

Stream: Watch Full Movies @ LimeMovies

Last Updated: 2025-12-02 07:03:06 (Update Now)

Torrent added: 2024-12-02 21:02:34






Torrent Files List


Get Bonus Downloads Here.url (Size: 1.05 GB) (Files: 71)

 Get Bonus Downloads Here.url

0.18 KB

 ~Get Your Files Here !

  001. Part 1.mp4

1.56 MB

  002. Chapter 1. Introduction.mp4

7.38 MB

  003. Chapter 1. Why large datasets.mp4

7.41 MB

  004. Chapter 1. What is parallel computing.mp4

15.57 MB

  005. Chapter 1. The map and reduce style.mp4

14.55 MB

  006. Chapter 1. Distributed computing for speed and scale.mp4

6.69 MB

  007. Chapter 1. Hadoop A distributed framework for map and reduce.mp4

6.19 MB

  008. Chapter 1. Spark for high-powered map, reduce, and more.mp4

3.05 MB

  009. Chapter 1. AWS Elastic MapReduce Large datasets in the cloud.mp4

3.33 MB

  010. Chapter 1. Summary.mp4

3.04 MB

  011. Chapter 2. Accelerating large dataset work Map and parallel computing.mp4

28.88 MB

  012. Chapter 2. Parallel processing.mp4

57.44 MB

  013. Chapter 2. Putting it all together Scraping a Wikipedia network.mp4

33.79 MB

  014. Chapter 2. Exercises.mp4

6.64 MB

  015. Chapter 2. Summary.mp4

2.79 MB

  016. Chapter 3. Function pipelines for mapping complex transformations.mp4

10.88 MB

  017. Chapter 3. Unmasking hacker communications.mp4

27.88 MB

  018. Chapter 3. Twitter demographic projections.mp4

45.96 MB

  019. Chapter 3. Exercises.mp4

4.87 MB

  020. Chapter 3. Summary.mp4

2.68 MB

  021. Chapter 4. Processing large datasets with lazy workflows.mp4

6.35 MB

  022. Chapter 4. Some lazy functions to know.mp4

12.63 MB

  023. Chapter 4. Understanding iterators The magic behind lazy Python.mp4

18.51 MB

  024. Chapter 4. The poetry puzzle Lazily processing a large dataset.mp4

29.85 MB

  025. Chapter 4. Lazy simulations Simulating fishing villages.mp4

27.16 MB

  026. Chapter 4. Exercises.mp4

6.16 MB

  027. Chapter 4. Summary.mp4

4.06 MB

  028. Chapter 5. Accumulation operations with reduce.mp4

6.60 MB

  029. Chapter 5. The three parts of reduce.mp4

25.10 MB

  030. Chapter 5. Reductions you re familiar with.mp4

10.34 MB

  031. Chapter 5. Using map and reduce together.mp4

12.44 MB

  032. Chapter 5. Analyzing car trends with reduce.mp4

22.15 MB

  033. Chapter 5. Speeding up map and reduce.mp4

3.96 MB

  034. Chapter 5. Exercises.mp4

3.74 MB

  035. Chapter 5. Summary.mp4

3.04 MB

  036. Chapter 6. Speeding up map and reduce with advanced parallelization.mp4

25.99 MB

  037. Chapter 6. Solving the parallel map and reduce paradox.mp4

48.98 MB

  038. Chapter 6. Summary.mp4

3.06 MB

  039. Part 2.mp4

2.01 MB

  040. Chapter 7. Processing truly big datasets with Hadoop and Spark.mp4

11.87 MB

  041. Chapter 7. Hadoop for batch processing.mp4

13.49 MB

  042. Chapter 7. Using Hadoop to find high-scoring words.mp4

14.49 MB

  043. Chapter 7. Spark for interactive workflows.mp4

18.57 MB

  044. Chapter 7. Document word scores in Spark.mp4

19.80 MB

  045. Chapter 7. Exercises.mp4

3.54 MB

  046. Chapter 7. Summary.mp4

2.66 MB

  047. Chapter 8. Best practices for large data with Apache Streaming and mrjob.mp4

9.92 MB

  048. Chapter 8. Tennis analytics with Hadoop.mp4

28.34 MB

  049. Chapter 8. mrjob for Pythonic Hadoop streaming.mp4

25.03 MB

  050. Chapter 8. Tennis match analysis with mrjob.mp4

24.70 MB

  051. Chapter 8. Exercises.mp4

2.76 MB

  052. Chapter 8. Summary.mp4

3.16 MB

  053. Chapter 9. PageRank with map and reduce in PySpark.mp4

32.59 MB

  054. Chapter 9. Tennis rankings with Elo and PageRank in PySpark.mp4

51.09 MB

  055. Chapter 9. Exercises.mp4

4.74 MB

  056. Chapter 9. Summary.mp4

3.08 MB

  057. Chapter 10. Faster decision-making with machine learning and PySpark.mp4

25.98 MB

  058. Chapter 10. Machine learning basics with decision tree classifiers.mp4

40.69 MB

  059. Chapter 10. Fast random forest classifications in PySpark.mp4

15.40 MB

  060. Chapter 10. Summary.mp4

2.92 MB

  061. Part 3.mp4

2.11 MB

  062. Chapter 11. Large datasets in the cloud with Amazon Web Services and S3.mp4

23.24 MB

  063. Chapter 11. Storing data in the cloud with S3.mp4

40.96 MB

  064. Chapter 11. Exercises.mp4

2.44 MB

  065. Chapter 11. Summary.mp4

3.21 MB

  066. Chapter 12. MapReduce in the cloud with Amazon s Elastic MapReduce.mp4

33.20 MB

  067. Chapter 12. Machine learning in the cloud with Spark on EMR.mp4

47.21 MB

  068. Chapter 12. Exercises.mp4

2.47 MB

  069. Chapter 12. Summary.mp4

3.42 MB

  Bonus Resources.txt

0.38 KB
 

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