Description

Do you want to learn data management and machine learning with PySpark? This is the course where you will learn about Big Data Analytics with PySpark and learn crucial techniques to implement this knowledge. In this course, you will learn how to operate PySpark. Basically, this is the package of Python that helps in solving data on a large scale. After doing this course, you will start performing ML tasks smoothly.

What should you learn PySpark from us?

You should take our course because this will help you to learn many things about PySpark which are truly essential for today's world. Here are the few course highlights topics we will cover.

  • You will gain expertise in the concepts of vital data analytics
  • creating data visualizations with the help of Jupyter
  • managing and working with a large set of databases.
  • Learning how to create a fast and scalable machine learning application 
  • Learning how to load data fastly
  • performing data analysis tasks effectively
  • learning data streams with spark streaming
  • Mastering in big data analytics

So, at the end of the course, you will have sound knowledge of Big data analytics with PySpark and will perform several machine learning work effectively even you have to face a large dataset. Also, you have tips and some crucial tips that experts use for doing machine learning tasks.

Course Content

Total: 39 lectures
  • Python versus Spark
  • Preparing for the Course
  • Connecting Jupyter to Spark
  • Getting to Know Spark
  • The Power of Spark
  • The Power of Spark MLlib
  • Spark DataFrames
  • Spark Data Operations
  • Loading Data from CSV Files
  • Fixing Issues in Our Data – Part One
  • Fixing Issues in Our Data – Part Two
  • Grouping, Joining, and Aggregating – Part One
  • Grouping, Joining, and Aggregating – Part Two
  • Machine Learning with Spark
  • Building a Recommendation System with Spark MLlib – Part One
  • Building a Recommendation System with Spark MLlib – Part Two
  • Building a Recommendation System with Spark MLlib – Part Three
  • Finalizing our Recommendation System
  • What We Have Learned So Far
  • Machine Learning with Spark
  • Machine Learning Pipelines
  • Running a Logistic Regression Pipeline
  • Parameters, Features, and Persistence
  • Frequent Pattern Mining and Statistics
  • Natural Language Processing with Spark
  • Identifying Our Data
  • Data Preparation and Exploration
  • Creating Our Raw Training Data
  • Data Preparation and Regular Expressions
  • Data Cleaning and Transformation
  • Training a Sentiment Analysis Model – Part One
  • Training a Sentiment Analysis Model – Part Two
  • Fetching Data from Twitter
  • Spark Structured Streaming
  • Managing and Converting Streams
  • Assembling Our Streaming ML Solution
  • A Structured Approach to ML Streaming
  • Running Spark in Production
  • Running Spark at Scale

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