Machine Learning with Go Training primarily focus on building simple, maintainable and ease for deploying Machine Learning Application. Go Programming Language, one of the foremost language for configuring and deploying ML Application as compared to other programming languages.


  • Understanding the basics and advanced level of ML concept with Go
  • Understanding the concept of building simple, but powerful ML Application
  • Integration of different ML Models in Go Applications


  • Expertise in Data Gathering, organization, parsing and Cleaning
  • Handling evaluation and validation of models
  • Optimization in ML workflow techniques
Duration: 2 Days

Course Content:
  1. Concept of Gathering and Data Organization

    • Overview of Handling Data
    • Understanding the best practices for gathering and organizing Data
    • Dealing with CSV Files
    • Dealing with JSON Files
    • Dealing with SQL-like Database
    • Overview of Caching
    • Introducing the concept of Data Versioning
  2. Understanding the concept of Matrices, Probability and Statistics

    • Using Matrices and vectors

      • Using Vectors
      • Understanding Vector Operation
      • Using Matrices
      • Understanding Matrices Operation
    • Using Statistics

      • Concept of Distributions
      • Understanding Statistical Measures
      • Distribution Visualization
    • Using Probability

      • Understanding the concept of Random Variables
      • Dealing with Probability Measures
      • Concept of Independent and Conditional Probability
      • Introducing Hypothesis testing
  3. Overview of Evaluation and Validation

    • Understanding Evaluation

      • Concept of Continuous Metrics
      • Concept of Categorical Metrics
    • Understanding Validation

      • Dealing with Training and test sets
      • Using Holdout set
      • Using Cross Validation
  4. Introducing the concept of Classification

    • Overview of Classification Model jargon
    • Understanding Logistic Regression
    • Understanding k-nearest neighbors
    • Overview of Decision Trees and Random Forests
    • Understanding Naive Bayes
  5. Introducing the concept of Clustering

    • Overview of Clustering Model jargon
    • Distance Measurement or Similarity
    • Evaluation of Clustering Techniques
    • Understanding k-means clustering
  1. Overview of Time Series and Anomaly Detection

    • Time Series Representation in Go
    • Overview of Time Series jargon
    • Concept of statistics related to time series
    • Concept of Auto-regressive model for forecasting
    • Understanding Auto-regressive moving averages and other time series models
  2. Understanding the concept of Neural Network and Deep Learning

    • Basics of neural net jargon
    • Building simple neural network
    • Utilization of simple neural network
    • Basic Introduction to Deep Learning
  3. Deployment and Distribution of Analyses and Models

    • Concept of Running Models on Remote Machines
    • Dockerizing a machine learning Application
    • Building scalable and reproducible ML pipeline