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Machine Learning with Go Training

Machine Learning used with Go Programming Language creates the ML Application using features of Go Language in more secure way.

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Duration: 2 Days

Course fee:$399.00 (₹0)

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Product Description

Go Language is Open Source Programming Language which in turn making it simple for building easy, reliable and complete efficient Softwares. Using the combination of Machine Learning with Go, Applications are created which can be used for better responsive effects in the complete scenario. Machine Learning with Go Training indulges in 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. In the training, information on clustering is explained in detail form where the Clustering Model Jardon is introduced. The training primarily focuses on simple and advanced neural networks. Analysis on their responding models are taken into consideration for their proper and effective response. On the completion of ML with Go Language Training, the aspirants grasps the knowledge of developing and deploying ML Application.

Objectives

  • 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

Advantages

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

 

Additional Information

Day 1

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

Day 2

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

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