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Course Skill Level:

Foundational to Intermediate

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 basic programming and IT skills

Who should attend & recommended skills

  • This course is for those who want to Get basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering
  • Skill-level: Foundation-level Machine Learning with Go for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-5 years’ experience)
  • Programming: Basic (1-2 years’ experience)

About this course

Machine learning is an essential part of today’s data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This course will teach you how to efficiently develop machine learning applications in Go. The course starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced. The course then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum. The course also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring. At the end of the course, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by Machine Learning with Go instructor, students will learn about and explore:
  • Your handy guide to building machine learning workflows in Go for real-world scenarios
  • Building predictive models using the popular supervised and unsupervised machine learning techniques
  • Deployment strategies and taking your ML application from prototype to production ready.
  • The types of problem that machine learning solves, and the various approaches
  • Importing, pre-processing, and exploring data with Go to make it ready for machine learning algorithms
  • Visualizing data with gonum/plot and Gophernotes
  • Diagnosing common machine learning problems, such as overfitting and underfitting
  • Implementing supervised and unsupervised learning algorithms using Go libraries
  • Building a simple web service around a model and use it to make predictions

Course breakdown / modules

  • What is ML?
  • Types of ML algorithms
  • Why write ML applications in Go?
  • ML development life cycle

  • Installing Go
  • Running Go interactively with gophernotes
  • Example the most common phrases in positive and negative reviews
  • Example exploring body mass index data with gonum/plot
  • Example preprocessing data with Gota

  • Classification
  • Regression

  • Clustering
  • Principal component analysis

  • How to restore a saved GoML model
  • Deciding when to adopt a polyglot approach
  • Example invoking a Python model using os/exec
  • Example invoking a Python model using HTTP
  • Example deep learning using the TensorFlow API for Go

  • The continuous delivery feedback loop
  • Deployment models for ML applications

  • When to use ML
  • Typical stages in a ML project
  • When to combine ML with traditional code