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Machine Learning with Go, 2 Days

  • Course Code: Artificial Intelligence - Machine Learning with Go, 2 Days
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for those who wants 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

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Machine Learning with Go for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants 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 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

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 
  • Build predictive models using the popular supervised and unsupervised machine learning techniques 
  • Learn all about deployment strategies and take your ML application from prototype to production ready. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Understand the types of problem that machine learning solves, and the various approaches 
  • Import, pre-process, and explore data with Go to make it ready for machine learning algorithms 
  • Visualize data with gonum/plot and Gophernotes 
  • Diagnose common machine learning problems, such as overfitting and underfitting 
  • Implement supervised and unsupervised learning algorithms using Go libraries 
  • Build a simple web service around a model and use it to make predictions 

Audience & Pre-Requisites 

This course is for readers 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 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. 
  • Good foundational mathematics or logic skills 
  • For readers with existing programming skills. 

Course Agenda / Topics 

  1. Introducing Machine Learning with Go 
  • Introducing Machine Learning with Go 
  • What is ML? 
  • Types of ML algorithms 
  • Why write ML applications in Go? 
  • ML development life cycle 
  1. Setting Up the Development Environment 
  • Setting Up the Development Environment 
  • 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 
  1. Supervised Learning 
  • Supervised Learning 
  • Classification 
  • Regression 
  1. Unsupervised Learning 
  • Unsupervised Learning 
  • Clustering 
  • Principal component analysis 
  1. Using Pretrained Models 
  • Using Pretrained Models 
  • 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 
  1. Deploying Machine Learning Applications 
  • Deploying Machine Learning Applications 
  • The continuous delivery feedback loop 
  • Deployment models for ML applications 
  1. Conclusion – Successful ML Projects 
  • Conclusion – Successful ML Projects 
  • When to use ML 
  • Typical stages in a ML project 
  • When to combine ML with traditional code 
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