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Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Programmers with Apache and Linux knowledge

Who should attend & recommended skills

  • 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.
  • Skill-level: Foundation-level Machine Learning With Go skills for Intermediate skilled team members. This is not a basic class.
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning With Go expert instructor, students will learn about and explore:
  • Building simple, maintainable, and easy to deploy machine learning applications with popular Go packages
  • The statistics, algorithms, and techniques to implement machine learning
  • Overcoming the common challenges faced while deploying and scaling the machine learning workflows
  • Becoming well versed with data processing, parsing, and cleaning using Go packages
  • Gathering data from various sources and in various real-world formats
  • Performing regression, classification, and image processing with neural networks
  • Evaluating and detecting anomalies in a time series model
  • Common deep learning architectures to learn how each model is built
  • Optimizing, building, and scaling machine learning workflows
  • The best practices for machine learning model tuning for successful deployments

Course breakdown / modules

  • Handling data Gopher style
  • Best practices for gathering and organizing data with Go
  • CSV files
  • Web scraping
  • JSON
  • SQL-like databases
  • Caching
  • Data versioning

  • Matrices and vectors
  • Statistics
  • Probability

  • Evaluating
  • Validating

  • Understanding regression model jargon
  • Linear regression
  • Multiple linear regression
  • Nonlinear and other types of regression

  • Understanding classification model jargon
  • Logistic regression
  • k-nearest neighbors
  • Decision trees and random forests
  • Naive Bayes

  • Understanding clustering model jargon
  • Measuring distance or similarity
  • Evaluating clustering techniques
  • k-means clustering
  • Other clustering techniques