Let us help you find the training program you are looking for.

If you can't find what you are looking for, contact us, we'll help you find it. We have over 800 training programs to choose from.

Machine Learning With Go, 3 Days

  • Course Code: Artificial Intelligence - Machine Learning With Go
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants to Infuse an extra layer of intelligence into your Go applications with machine learning and AI

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning With Go skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Infuse an extra layer of intelligence into your Go applications with machine learning and AI 
  • 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. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

This popular Machine Learning With Go shows you how to overcome the common challenges of integrating analysis and machine learning code within an existing engineering organization. Machine Learning With Go, , will begin by helping you gain an understanding of how to gather, organize, and parse real-world data from a variety of sources. The course also provides absolute coverage in developing groundbreaking machine learning pipelines including predictive models, data visualizations, and statistical techniques. Up next, you will learn the thorough utilization of Golang libraries including golearn, gorgonia, gosl, hector, and mat64. You will discover the various TensorFlow capabilities, along with building simple neural networks and integrating them into machine learning models. You will also gain hands-on experience implementing essential machine learning techniques such as regression, classification, and clustering with the relevant Go packages. Furthermore, you will deep dive into the various Go tools that help you build deep neural networks. Lastly, you will become well versed with best practices for machine learning model tuning and optimization. By the end of the course, you will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations. 

Working in a hands-on learning environment, led by our Machine Learning With Go expert instructor, students will learn about and explore: 

  • Build simple, maintainable, and easy to deploy machine learning applications with popular Go packages 
  • Learn the statistics, algorithms, and techniques to implement machine learning 
  • Overcome the common challenges faced while deploying and scaling the machine learning workflows 

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

  • Become well versed with data processing, parsing, and cleaning using Go packages 
  • Learn to gather data from various sources and in various real-world formats 
  • Perform regression, classification, and image processing with neural networks 
  • Evaluate and detect anomalies in a time series model 
  • Understand common deep learning architectures to learn how each model is built 
  • Learn how to optimize, build, and scale machine learning workflows 
  • Discover the best practices for machine learning model tuning for successful deployments 

Audience & Pre-Requisites 

This course is geared for attendees with Apache knowledge who wish to Infuse an extra layer of intelligence into your Go applications with machine learning and AI 

Pre-Requisites:  Students should have  

  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Gathering and Organizing Data 
  • Gathering and Organizing Data 
  • Handling data – Gopher style 
  • Best practices for gathering and organizing data with Go 
  • CSV files 
  • Web scraping  
  • JSON 
  • SQL-like databases 
  • Caching 
  • Data versioning 
  1. Matrices, Probability, and Statistics 
  • Matrices, Probability, and Statistics 
  • Matrices and vectors 
  • Statistics 
  • Probability 
  1. Evaluating and Validating 
  • Evaluating and Validating 
  • Evaluating 
  • Validating 
  1. Regression 
  • Regression 
  • Understanding regression model jargon 
  • Linear regression 
  • Multiple linear regression 
  • Nonlinear and other types of regression 
  1. Classification 
  • Classification 
  • Understanding classification model jargon 
  • Logistic regression 
  • k-nearest neighbors  
  • Decision trees and random forests 
  • Naive Bayes 
  1. Clustering 
  • Clustering 
  • Understanding clustering model jargon 
  • Measuring distance or similarity 
  • Evaluating clustering techniques 
  • k-means clustering 
  • Other clustering techniques 
  1. Time Series and Anomaly Detection 
  • Time Series and Anomaly Detection 
  • Representing time series data in Go 
  • Understanding time series jargon 
  • Statistics related to time series 
  • Auto-regressive models for forecasting 
  • Auto-regressive moving averages and other time series models 
  • Anomaly detection 
  1. Neural Networks 
  • Neural Networks 
  • Understanding neural net jargon 
  • Building a simple neural network 
  • Utilizing the simple neural network 
  1. Deep Learning 
  • Deep Learning 
  • Deep learning techniques and jargon 
  • Deep learning with Go 
  1. Deploying and Distributing Analyses and Models 
  • Deploying and Distributing Analyses and Models 
  • Running models reliably on remote machines 
  • Building a scalable and reproducible machine learning pipeline 
View All Courses

    Course Inquiry

    Fill in the details below and we will get back to you as quickly as we can.

    Interested in any of these related courses?