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Using Spark with Python

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Curriculum For This Course

  • 1. Local Installation VirtualBox Part 1 4m 23s
  • 2. Local Installation VirtualBox Part 2 12m 59s
  • 3. Setting up PySpark 5m 9s
  • 1. AWS EC2 Set-up Guide 2m 38s
  • 2. Creating the EC2 Instance 14m 3s
  • 3. SSH with Mac or Linux 3m 41s
  • 4. Installations on EC2 14m 16s
  • 1. Introduction to Python Crash Course 1m 25s
  • 2. Jupyter Notebook Overview 5m 36s
  • 3. Python Crash Course Part One 15m 52s
  • 4. Python Crash Course Part Two 11m 53s
  • 5. Python Crash Course Part Three 10m 58s
  • 6. Python Crash Course Exercises 1m 8s
  • 7. Python Crash Course Exercise Solutions 9m 9s
  • 1. Introduction to Spark DataFrames 2m 16s
  • 2. Spark DataFrame Basics 10m 33s
  • 3. Spark DataFrame Basics Part Two 9m 37s
  • 4. Spark DataFrame Basic Operations 9m 53s
  • 5. Groupby and Aggregate Operations 11m 37s
  • 6. Missing Data 8m 22s
  • 7. Dates and Timestamps 9m 42s
  • 1. DataFrame Project Exercise 2m 40s
  • 2. DataFrame Project Exercise Solutions 16m 36s
  • 1. Introduction to Machine Learning and ISLR 4m 23s
  • 2. Machine Learning with Spark and Python with MLlib 4m 16s
  • 1. Linear Regression Theory and Reading 4m 55s
  • 2. Linear Regression Documentation Example 13m 32s
  • 3. Regression Evaluation 6m 36s
  • 4. Linear Regression Example Code Along 14m 15s
  • 5. Linear Regression Consulting Project 3m 3s
  • 6. Linear Regression Consulting Project Solutions 15m 3s
  • 1. Logistic Regression Theory and Reading 11m 13s
  • 2. Logistic Regression Example Code Along 15m 29s
  • 3. Logistic Regression Code Along 3m 5s
  • 4. Logistic Regression Consulting Project 11m
  • 5. Logistic Regression Consulting Project Solutions 7m 32s
  • 1. Tree Methods Theory and Reading 7m 50s
  • 2. Tree Methods Documentation Examples 12m 31s
  • 3. Decision Tress and Random Forest Code Along Examples 20m 24s
  • 4. Random Forest - Classification Consulting Project 2m 25s
  • 5. Random Forest Classification Consulting Project Solutions 7m 48s
  • 1. K-means Clustering Theory and Reading 6m 44s
  • 2. KMeans Clustering Documentation Example 9m 3s
  • 3. Clustering Example Code Along 10m 50s
  • 4. Clustering Consulting Project 3m
  • 5. Clustering Consulting Project Solutions 8m 26s
  • 1. Introduction to Recommender Systems 6m 24s
  • 2. Recommender System - Code Along Project 11m 40s
  • 1. Introduction to Natural Language Processing 7m 56s
  • 2. NLP Tools Part One 15m 30s
  • 3. NLP Tools Part Two 7m 48s
  • 4. Natural Language Processing Code Along Project 13m 27s
  • 1. Introduction to Streaming with Spark! 10m 11s
  • 2. Spark Streaming Documentation Example 8m 56s
  • 3. Spark Streaming Twitter Project - Part 3m 44s
  • 4. Spark Streaming Twitter Project - Part Two 12m 35s
  • 5. Spark Streaming Twitter Project - Part Three 13m 46s

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