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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
Magnet: Magnet Download
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
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001. Part 1.mp4
002. Chapter 1. Introduction.mp4
003. Chapter 1. Why large datasets.mp4
004. Chapter 1. What is parallel computing.mp4
005. Chapter 1. The map and reduce style.mp4
006. Chapter 1. Distributed computing for speed and scale.mp4
007. Chapter 1. Hadoop A distributed framework for map and reduce.mp4
008. Chapter 1. Spark for high-powered map, reduce, and more.mp4
009. Chapter 1. AWS Elastic MapReduce Large datasets in the cloud.mp4
010. Chapter 1. Summary.mp4
011. Chapter 2. Accelerating large dataset work Map and parallel computing.mp4
012. Chapter 2. Parallel processing.mp4
013. Chapter 2. Putting it all together Scraping a Wikipedia network.mp4
014. Chapter 2. Exercises.mp4
015. Chapter 2. Summary.mp4
016. Chapter 3. Function pipelines for mapping complex transformations.mp4
017. Chapter 3. Unmasking hacker communications.mp4
018. Chapter 3. Twitter demographic projections.mp4
019. Chapter 3. Exercises.mp4
020. Chapter 3. Summary.mp4
021. Chapter 4. Processing large datasets with lazy workflows.mp4
022. Chapter 4. Some lazy functions to know.mp4
023. Chapter 4. Understanding iterators The magic behind lazy Python.mp4
024. Chapter 4. The poetry puzzle Lazily processing a large dataset.mp4
025. Chapter 4. Lazy simulations Simulating fishing villages.mp4
026. Chapter 4. Exercises.mp4
027. Chapter 4. Summary.mp4
028. Chapter 5. Accumulation operations with reduce.mp4
029. Chapter 5. The three parts of reduce.mp4
030. Chapter 5. Reductions you re familiar with.mp4
031. Chapter 5. Using map and reduce together.mp4
032. Chapter 5. Analyzing car trends with reduce.mp4
033. Chapter 5. Speeding up map and reduce.mp4
034. Chapter 5. Exercises.mp4
035. Chapter 5. Summary.mp4
036. Chapter 6. Speeding up map and reduce with advanced parallelization.mp4
037. Chapter 6. Solving the parallel map and reduce paradox.mp4
038. Chapter 6. Summary.mp4
039. Part 2.mp4
040. Chapter 7. Processing truly big datasets with Hadoop and Spark.mp4
041. Chapter 7. Hadoop for batch processing.mp4
042. Chapter 7. Using Hadoop to find high-scoring words.mp4
043. Chapter 7. Spark for interactive workflows.mp4
044. Chapter 7. Document word scores in Spark.mp4
045. Chapter 7. Exercises.mp4
046. Chapter 7. Summary.mp4
047. Chapter 8. Best practices for large data with Apache Streaming and mrjob.mp4
048. Chapter 8. Tennis analytics with Hadoop.mp4
049. Chapter 8. mrjob for Pythonic Hadoop streaming.mp4
050. Chapter 8. Tennis match analysis with mrjob.mp4
051. Chapter 8. Exercises.mp4
052. Chapter 8. Summary.mp4
053. Chapter 9. PageRank with map and reduce in PySpark.mp4
054. Chapter 9. Tennis rankings with Elo and PageRank in PySpark.mp4
055. Chapter 9. Exercises.mp4
056. Chapter 9. Summary.mp4
057. Chapter 10. Faster decision-making with machine learning and PySpark.mp4
058. Chapter 10. Machine learning basics with decision tree classifiers.mp4
059. Chapter 10. Fast random forest classifications in PySpark.mp4
060. Chapter 10. Summary.mp4
061. Part 3.mp4
062. Chapter 11. Large datasets in the cloud with Amazon Web Services and S3.mp4
063. Chapter 11. Storing data in the cloud with S3.mp4
064. Chapter 11. Exercises.mp4
065. Chapter 11. Summary.mp4
066. Chapter 12. MapReduce in the cloud with Amazon s Elastic MapReduce.mp4
067. Chapter 12. Machine learning in the cloud with Spark on EMR.mp4
068. Chapter 12. Exercises.mp4
069. Chapter 12. Summary.mp4
Bonus Resources.txt
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