[FreeAllCourse.Com] Udemy - Spark and Python for Big Data with PySpark

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[FreeAllCourse.Com] Udemy - Spark and Python for Big Data with PySpark

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文件大小： 1.56G
已经下载：1137次
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收录时间：2022-01-09
最近下载：2024-12-25

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文件列表

  • 4. AWS EC2 PySpark Set-up/2. Creating the EC2 Instance.mp4 66.0 MB
  • 17. Spark Streaming with Python/5. Spark Streaming Twitter Project - Part Three.mp4 57.7 MB
  • 12. Logistic Regression/2. Logistic Regression Example Code Along.mp4 56.0 MB
  • 10. Introduction to Machine Learning with MLlib/2. Machine Learning with Spark and Python with MLlib.mp4 53.8 MB
  • 4. AWS EC2 PySpark Set-up/4. Installations on EC2.mp4 52.9 MB
  • 13. Decision Trees and Random Forests/3. Decision Tress and Random Forest Code Along Examples.mp4 51.5 MB
  • 1. Introduction to Course/4. What is Spark Why Python.mp4 50.5 MB
  • 3. Local VirtualBox Set-up/2. Local Installation VirtualBox Part 2.mp4 48.7 MB
  • 6. AWS EMR Cluster Setup/1. AWS EMR Setup.mp4 47.5 MB
  • 9. Spark DataFrame Project Exercise/2. DataFrame Project Exercise Solutions.mp4 47.2 MB
  • 12. Logistic Regression/3. Logistic Regression Code Along.mp4 43.5 MB
  • 11. Linear Regression/2. Linear Regression Documentation Example.mp4 42.6 MB
  • 11. Linear Regression/4. Linear Regression Example Code Along.mp4 41.1 MB
  • 11. Linear Regression/6. Linear Regression Consulting Project Solutions.mp4 40.6 MB
  • 3. Local VirtualBox Set-up/1. Local Installation VirtualBox Part 1.mp4 39.5 MB
  • 16. Natural Language Processing/2. NLP Tools Part One.mp4 37.9 MB
  • 16. Natural Language Processing/4. Natural Language Processing Code Along Project.mp4 36.9 MB
  • 13. Decision Trees and Random Forests/2. Tree Methods Documentation Examples.mp4 35.8 MB
  • 12. Logistic Regression/5. Logistic Regression Consulting Project Solutions.mp4 35.6 MB
  • 17. Spark Streaming with Python/1. Introduction to Streaming with Spark!.mp4 34.2 MB
  • 7. Python Crash Course/3. Python Crash Course Part One.mp4 31.0 MB
  • 17. Spark Streaming with Python/4. Spark Streaming Twitter Project - Part Two.mp4 30.7 MB
  • 8. Spark DataFrame Basics/5. Groupby and Aggregate Operations.mp4 30.2 MB
  • 17. Spark Streaming with Python/2. Spark Streaming Documentation Example.mp4 30.0 MB
  • 14. K-means Clustering/3. Clustering Example Code Along.mp4 29.2 MB
  • 5. Databricks Setup/1. Databricks Setup.mp4 29.0 MB
  • 8. Spark DataFrame Basics/4. Spark DataFrame Basic Operations.mp4 28.9 MB
  • 7. Python Crash Course/7. Python Crash Course Exercise Solutions.mp4 26.3 MB
  • 15. Collaborative Filtering for Recommender Systems/2. Recommender System - Code Along Project.mp4 25.8 MB
  • 8. Spark DataFrame Basics/7. Dates and Timestamps.mp4 25.3 MB
  • 7. Python Crash Course/5. Python Crash Course Part Three.mp4 24.3 MB
  • 14. K-means Clustering/5. Clustering Consulting Project Solutions.mp4 24.2 MB
  • 7. Python Crash Course/4. Python Crash Course Part Two.mp4 23.3 MB
  • 8. Spark DataFrame Basics/2. Spark DataFrame Basics.mp4 22.1 MB
  • 14. K-means Clustering/2. KMeans Clustering Documentation Example.mp4 21.9 MB
  • 12. Logistic Regression/1. Logistic Regression Theory and Reading.mp4 21.7 MB
  • 8. Spark DataFrame Basics/3. Spark DataFrame Basics Part Two.mp4 20.7 MB
  • 10. Introduction to Machine Learning with MLlib/1. Introduction to Machine Learning and ISLR.mp4 19.8 MB
  • 16. Natural Language Processing/3. NLP Tools Part Two.mp4 19.8 MB
  • 8. Spark DataFrame Basics/6. Missing Data.mp4 18.1 MB
  • 13. Decision Trees and Random Forests/5. Random Forest Classification Consulting Project Solutions.mp4 16.7 MB
  • 3. Local VirtualBox Set-up/3. Setting up PySpark.mp4 16.3 MB
  • 13. Decision Trees and Random Forests/1. Tree Methods Theory and Reading.mp4 15.4 MB
  • 1. Introduction to Course/2. Course Overview.mp4 15.1 MB
  • 16. Natural Language Processing/1. Introduction to Natural Language Processing.mp4 15.0 MB
  • 7. Python Crash Course/2. Jupyter Notebook Overview.mp4 13.9 MB
  • 14. K-means Clustering/1. K-means Clustering Theory and Reading.mp4 13.5 MB
  • 15. Collaborative Filtering for Recommender Systems/1. Introduction to Recommender Systems.mp4 13.3 MB
  • 11. Linear Regression/3. Regression Evaluation.mp4 12.5 MB
  • 9. Spark DataFrame Project Exercise/1. DataFrame Project Exercise.mp4 12.5 MB
  • 17. Spark Streaming with Python/3. Spark Streaming Twitter Project - Part.mp4 12.4 MB
  • 1. Introduction to Course/1. Introduction.mp4 12.2 MB
  • 2. Setting up Python with Spark/1. Set-up Overview.mp4 11.4 MB
  • 11. Linear Regression/1. Linear Regression Theory and Reading.mp4 10.4 MB
  • 4. AWS EC2 PySpark Set-up/3. SSH with Mac or Linux.mp4 9.7 MB
  • 11. Linear Regression/5. Linear Regression Consulting Project.mp4 7.1 MB
  • 14. K-means Clustering/4. Clustering Consulting Project.mp4 6.9 MB
  • 12. Logistic Regression/4. Logistic Regression Consulting Project.mp4 6.6 MB
  • 13. Decision Trees and Random Forests/4. Random Forest - Classification Consulting Project.mp4 5.7 MB
  • 4. AWS EC2 PySpark Set-up/1. AWS EC2 Set-up Guide.mp4 5.6 MB
  • 7. Python Crash Course/6. Python Crash Course Exercises.mp4 5.3 MB
  • 8. Spark DataFrame Basics/1. Introduction to Spark DataFrames.mp4 4.9 MB
  • 7. Python Crash Course/1. Introduction to Python Crash Course.mp4 3.2 MB
  • 1. Introduction to Course/2.2 Python-and-Spark-for-Big-Data-master.zip.zip 1.7 MB
  • 1. Introduction to Course/3.1 Python-and-Spark-for-Big-Data-master.zip.zip 1.7 MB
  • 11. Linear Regression/4.1 Ecommerce_Customers.csv.csv 86.9 kB
  • 13. Decision Trees and Random Forests/3. Decision Tress and Random Forest Code Along Examples.srt 31.5 kB
  • 1. Introduction to Course/4. What is Spark Why Python.srt 31.2 kB
  • 17. Spark Streaming with Python/5. Spark Streaming Twitter Project - Part Three.srt 30.0 kB
  • 12. Logistic Regression/3. Logistic Regression Code Along.srt 28.0 kB
  • 6. AWS EMR Cluster Setup/1. AWS EMR Setup.srt 27.0 kB
  • 4. AWS EC2 PySpark Set-up/2. Creating the EC2 Instance.srt 25.0 kB
  • 7. Python Crash Course/3. Python Crash Course Part One.srt 24.4 kB
  • 12. Logistic Regression/2. Logistic Regression Example Code Along.srt 24.1 kB
  • 11. Linear Regression/4. Linear Regression Example Code Along.srt 23.8 kB
  • 16. Natural Language Processing/2. NLP Tools Part One.srt 23.5 kB
  • 11. Linear Regression/6. Linear Regression Consulting Project Solutions.srt 23.3 kB
  • 9. Spark DataFrame Project Exercise/2. DataFrame Project Exercise Solutions.srt 23.0 kB
  • 11. Linear Regression/2. Linear Regression Documentation Example.srt 22.5 kB
  • 13. Decision Trees and Random Forests/2. Tree Methods Documentation Examples.srt 21.2 kB
  • 4. AWS EC2 PySpark Set-up/4. Installations on EC2.srt 20.7 kB
  • 16. Natural Language Processing/4. Natural Language Processing Code Along Project.srt 20.0 kB
  • 8. Spark DataFrame Basics/5. Groupby and Aggregate Operations.srt 19.4 kB
  • 5. Databricks Setup/1. Databricks Setup.srt 19.4 kB
  • 3. Local VirtualBox Set-up/2. Local Installation VirtualBox Part 2.srt 19.3 kB
  • 14. K-means Clustering/3. Clustering Example Code Along.srt 18.9 kB
  • 3. Local VirtualBox Set-up/1. Local Installation VirtualBox Part 1.srt 18.7 kB
  • 17. Spark Streaming with Python/4. Spark Streaming Twitter Project - Part Two.srt 18.6 kB
  • 15. Collaborative Filtering for Recommender Systems/2. Recommender System - Code Along Project.srt 18.4 kB
  • 12. Logistic Regression/1. Logistic Regression Theory and Reading.srt 18.3 kB
  • 10. Introduction to Machine Learning with MLlib/1. Introduction to Machine Learning and ISLR.srt 18.0 kB
  • 7. Python Crash Course/4. Python Crash Course Part Two.srt 18.0 kB
  • 17. Spark Streaming with Python/1. Introduction to Streaming with Spark!.srt 17.9 kB
  • 17. Spark Streaming with Python/2. Spark Streaming Documentation Example.srt 17.8 kB
  • 8. Spark DataFrame Basics/2. Spark DataFrame Basics.srt 16.8 kB
  • 7. Python Crash Course/5. Python Crash Course Part Three.srt 16.4 kB
  • 8. Spark DataFrame Basics/4. Spark DataFrame Basic Operations.srt 16.4 kB
  • 10. Introduction to Machine Learning with MLlib/2. Machine Learning with Spark and Python with MLlib.srt 16.2 kB
  • 12. Logistic Regression/5. Logistic Regression Consulting Project Solutions.srt 15.6 kB
  • 1. Introduction to Course/2. Course Overview.srt 15.1 kB
  • 14. K-means Clustering/2. KMeans Clustering Documentation Example.srt 15.1 kB
  • 8. Spark DataFrame Basics/7. Dates and Timestamps.srt 15.0 kB
  • 8. Spark DataFrame Basics/3. Spark DataFrame Basics Part Two.srt 14.8 kB
  • 8. Spark DataFrame Basics/6. Missing Data.srt 13.8 kB
  • 16. Natural Language Processing/1. Introduction to Natural Language Processing.srt 13.5 kB
  • 7. Python Crash Course/7. Python Crash Course Exercise Solutions.srt 13.2 kB
  • 13. Decision Trees and Random Forests/5. Random Forest Classification Consulting Project Solutions.srt 12.0 kB
  • 13. Decision Trees and Random Forests/1. Tree Methods Theory and Reading.srt 12.0 kB
  • 14. K-means Clustering/5. Clustering Consulting Project Solutions.srt 11.8 kB
  • 7. Python Crash Course/2. Jupyter Notebook Overview.srt 11.6 kB
  • 11. Linear Regression/3. Regression Evaluation.srt 11.1 kB
  • 16. Natural Language Processing/3. NLP Tools Part Two.srt 11.0 kB
  • 14. K-means Clustering/1. K-means Clustering Theory and Reading.srt 10.9 kB
  • 2. Setting up Python with Spark/1. Set-up Overview.srt 10.4 kB
  • 15. Collaborative Filtering for Recommender Systems/1. Introduction to Recommender Systems.srt 10.3 kB
  • 3. Local VirtualBox Set-up/3. Setting up PySpark.srt 8.5 kB
  • 11. Linear Regression/1. Linear Regression Theory and Reading.srt 8.3 kB
  • 4. AWS EC2 PySpark Set-up/3. SSH with Mac or Linux.srt 7.6 kB
  • 17. Spark Streaming with Python/3. Spark Streaming Twitter Project - Part.srt 7.5 kB
  • 9. Spark DataFrame Project Exercise/1. DataFrame Project Exercise.srt 5.5 kB
  • 12. Logistic Regression/4. Logistic Regression Consulting Project.srt 5.4 kB
  • 11. Linear Regression/5. Linear Regression Consulting Project.srt 5.0 kB
  • 14. K-means Clustering/4. Clustering Consulting Project.srt 4.8 kB
  • 1. Introduction to Course/1. Introduction.srt 4.6 kB
  • 4. AWS EC2 PySpark Set-up/1. AWS EC2 Set-up Guide.srt 4.5 kB
  • 13. Decision Trees and Random Forests/4. Random Forest - Classification Consulting Project.srt 3.9 kB
  • 8. Spark DataFrame Basics/1. Introduction to Spark DataFrames.srt 3.7 kB
  • 7. Python Crash Course/6. Python Crash Course Exercises.srt 2.5 kB
  • 7. Python Crash Course/1. Introduction to Python Crash Course.srt 2.5 kB
  • Read Me.txt 1.0 kB
  • 18. Bonus/1. Bonus Lecture.html 532 Bytes
  • 1. Introduction to Course/3. Frequently Asked Questions.html 447 Bytes
  • 2. Setting up Python with Spark/2. Note on Installation Sections.html 347 Bytes
  • [FreeAllCourse.Com].URL 228 Bytes
  • 1. Introduction to Course/2.1 Course Overview Slides.html 161 Bytes
  • 1. Introduction to Course/4.1 Spark and Python Slides.html 161 Bytes
  • 10. Introduction to Machine Learning with MLlib/1.1 Slides for ML Intro.html 161 Bytes
  • 11. Linear Regression/1.1 Slides for Linear Regression.html 161 Bytes
  • 12. Logistic Regression/1.1 Slides for Logistic Regression.html 161 Bytes
  • 13. Decision Trees and Random Forests/1.1 Slides for Tree Methods.html 161 Bytes
  • 14. K-means Clustering/1.1 Slides for Clustering.html 161 Bytes
  • 15. Collaborative Filtering for Recommender Systems/1.1 Recommender Slides.html 161 Bytes
  • 16. Natural Language Processing/1.1 NLP Slides.html 161 Bytes
  • 17. Spark Streaming with Python/1.1 Spark Streaming Slides.html 161 Bytes
  • 2. Setting up Python with Spark/1.1 Slides for Installation Options Overview.html 161 Bytes
  • 2. Setting up Python with Spark/1.2 Slides for Installation.html 161 Bytes
  • 7. Python Crash Course/1.1 Slides for Python Crash Course.html 161 Bytes
  • 8. Spark DataFrame Basics/1.1 Slides for Spark DataFrame Basics.html 161 Bytes
  • 12. Logistic Regression/3.2 Great Example from Databricks.html 150 Bytes
  • 12. Logistic Regression/3.1 Explanation of AUC.html 148 Bytes

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