Big Data Analytics using Python and Apache Spark | Machine Learning Tutorial

Apache Spark is the most active Apache project, and it is pushing back Map Reduce. It is fast, general purpose and supports multiple programming languages, data sources and management systems. More and more organizations are adapting Apache Spark to build big data solutions through batch, interactive and stream processing paradigms. The demand for trained professionals in Spark is going through the roof. Being a new technology, there aren't enough training sources to provide easy guidance on building end-to-end solutions. Section 1: Introduction Lecture 1 About the course 08:42 Lecture 2 About V2 Maestros 01:39 Lecture 3 Resource Bundle Article Section 2: Overview Lecture 4 Hadoop Overview 10:06 Lecture 5 HDFS Architecture 14:46 Lecture 6 Map Reduce - How it works 17:24 Lecture 7 Map Reduce - Example 16:46 Lecture 8 Hadoop Stack 06:27 Lecture 9 What is Spark? 14:03 Lecture 10 Spark Architecture - Part 1 13:23 Lecture 11 Spark Architecture - Part 2 13:25 Lecture 12 Installing Spark and Setting up for Python 12:05 Quiz 1 Hadoop and Spark Architecture 5 questions Section 3: Programming with Spark Lecture 13 Spark Transformations 11:33 Lecture 14 Spark Actions 15:04 Lecture 15 Advanced Spark Programming 10:10 Lecture 16 Python - Spark Programming examples 1 16:11 Lecture 17 Python - Spark Programming Examples 2 17:18 Quiz 2 Data Engineering with Spark 5 questions Lecture 18 PRACTICE Exercise : Spark Operations Article Section 4: Spark SQL Lecture 19 Spark SQL Overview 10:03 Lecture 20 Python - Spark SQL Examples 16:16 Quiz 3 Spark SQL 2 questions Lecture 21 PRACTICE Exercise : Spark SQL Article Section 5: Spark Streaming Lecture 22 Streaming with Apache Spark 15:53 Lecture 23 Python - Spark Streaming examples 17:47 Quiz 4 Spark Streaming 3 questions Section 6: Real time Data Science Lecture 24 Basic Elements of Data Science 11:51 Lecture 25 The Dataset 10:44 Lecture 26 Learning from relationships 12:55 Lecture 27 Modeling and Prediction 09:31 Lecture 28 Data Science Use Cases 07:47 Lecture 29 Types of Analytics 12:08 Lecture 30 Types of Learning 17:16 Lecture 31 Doing Data Science in real time with Spark 07:39 Quiz 5 Spark Data Science 5 questions Section 7: Machine Learning with Spark Lecture 32 Spark Machine Learning 12:18 Lecture 33 Analyzing Results and Errors 13:46 Lecture 34 Linear Regression 19:00 Lecture 35 Spark Use Case : Linear Regression 18:33 Lecture 36 Decision Trees 10:42 Lecture 37 Spark Use Case : Decision Trees Classification 14:58 Lecture 38 Principal Component Analysis 07:28 Lecture 39 Random Forests Classification 10:31 Lecture 40 Python Use Case : Random Forests & PCA 13:16 Lecture 41 Text Preprocessing with TF-IDF 14:53 Lecture 42 Naive Bayes Classification 19:21 Lecture 43 Spark Use Case : Naive Bayes & TF-IDF 07:26 Lecture 44 K-Means Clustering 11:53 Lecture 45 Spark Use Case : K-Means 14:26 Lecture 46 Recommendation Engines 11:55 Lecture 47 Spark Use Case : Collaborative Filtering 06:34 Lecture 48 Real Time Twitter Data Sentiment Analysis 10:11 Quiz 6 Spark Machine Learning Algorithms 4 questions Lecture 49 PRACTICE Exercise : Spark Clustering Article Lecture 50 PRACTICE Exercise : Spark Classification Article Section 8: Conclusion Lecture 51 Closing Remarks 01:56 Lecture 52 BONUS Lecture : Other courses you should check out Article