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Course Skill Level:


Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Apache knowledge, basic big data & BI and Linux skills seeking to combine advanced analytics with modern scalable technologies to derive actionable insights

Who should attend & recommended skills

  • This course is geared for attendees with Apache knowledge who wish to combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time to help us manage and process big data.
  • We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data.
  • Skill-level: Foundation-level Machine Learning with Apache Spark skills for Intermediate skilled team members. This is not a basic class.
  • Big data business intelligence skills: Basic (1-2 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

Every person and every organization in the world manage data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently. But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it? The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning with Apache expert instructor, students will learn about and explore:
  • Making a hands-on start in the fields of Big Data, Distributed Technologies and Machine Learning
  • How to design, develop and interpret the results of common Machine Learning algorithms
  • Uncovering hidden patterns in your data in order to derive real actionable insights and business value
  • How Spark fits in the context of the big data ecosystem
  • How to deploy and configure a local development environment using Apache Spark
  • How to design supervised and unsupervised learning models
  • Building models to perform NLP, deep learning, and cognitive services using Spark ML libraries
  • Designing real-time machine learning pipelines in Apache Spark
  • Becoming familiar with advanced techniques for processing a large volume of data by applying machine learning algorithms

Course breakdown / modules

  • A brief history of data
  • Big data ecosystem

  • CentOS Linux 7 virtual machine

  • Artificial intelligence
  • Machine learning
  • Deep learning
  • NLP
  • Cognitive computing
  • Machine learning pipelines in Apache Spark

  • Linear regression
  • Logistic regression
  • Classification and Regression Trees

  • Clustering
  • Principal component analysis

  • Feature transformers
  • Feature extractors
  • Case study and sentiment analysis

  • Artificial neural networks

  • Distributed streamingplatform
  • Distributed stream processing engines
  • Stream processing pipeline