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A to Z (NLP) Machine Learning Model building and Deployment.

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A to Z (NLP) Machine Learning Model building and Deployment., available at $54.99, has an average rating of 4.45, with 31 lectures, 4 quizzes, based on 161 reviews, and has 13984 subscribers.

You will learn about Developing the NLP Model for Sentiment analysis and Machine learning deployment on local server using flask and docker. Select the most efficient Machine Learning Model,Tune the hyper-parameters and selecting the best model using cross-validation technique A quick discussion from the basic in nutshell about DevOps tools like docker, Git and GitLab, Jenkins etc. A better understanding about software development and automation in real scenario and concept of end-to-end Integration. This course is ideal for individuals who are Beginner Machine Learning Enthusiast want to deploy their model. or Beginner python developer curious about data science. or Any one wants to learn Devops and role of DevOps in Data Science. It is particularly useful for Beginner Machine Learning Enthusiast want to deploy their model. or Beginner python developer curious about data science. or Any one wants to learn Devops and role of DevOps in Data Science.

Enroll now: A to Z (NLP) Machine Learning Model building and Deployment.

Summary

Title: A to Z (NLP) Machine Learning Model building and Deployment.

Price: $54.99

Average Rating: 4.45

Number of Lectures: 31

Number of Quizzes: 4

Number of Published Lectures: 31

Number of Published Quizzes: 4

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 35

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • Developing the NLP Model for Sentiment analysis and Machine learning deployment on local server using flask and docker.
  • Select the most efficient Machine Learning Model,Tune the hyper-parameters and selecting the best model using cross-validation technique
  • A quick discussion from the basic in nutshell about DevOps tools like docker, Git and GitLab, Jenkins etc.
  • A better understanding about software development and automation in real scenario and concept of end-to-end Integration.

Who Should Attend

  • Beginner Machine Learning Enthusiast want to deploy their model.
  • Beginner python developer curious about data science.
  • Any one wants to learn Devops and role of DevOps in Data Science.

Target Audiences

  • Beginner Machine Learning Enthusiast want to deploy their model.
  • Beginner python developer curious about data science.
  • Any one wants to learn Devops and role of DevOps in Data Science.

Machine Learning Real value comes from actually deploying a machine learning solution into production and the necessary monitoring and optimization work that comes after it.

Most of the problems nowadays as I have made a machine-learning model but what next.

How it is available to the end-user, the answer is through API, but how it works?

How you can understand where the Docker stands and how to monitor the build we created.

This course has been designed to keep these areas under consideration. The combination of industry-standard build pipeline with some of the most common and important tools.

This course has been designed into Following sections:

1) Configure and a quick walkthrough of each of the tools and technologies we used in this course.

2) Building our NLP Machine Learning model and tune the hyperparameters.

3) Creating flask API and running the WebAPI in our Browser.

4) Creating the Docker file, build our image and running our ML Model in Docker container.

5) Configure GitLab and push your code in GitLab.

6) Configure Jenkins and write Jenkins’s file and run end-to-end Integration.

This course is perfect for you to have a taste of industry-standard Data Science and deploying in the local server. Hope you enjoy the course as I enjoyed making it.

Course Curriculum

Chapter 1: Installation and Configuration

Lecture 1: Introduction-Table Content

Lecture 2: Environment – Part 1) Virtual Box Configuration and Installation

Lecture 3: Environment -Part ) Putty setup in virtual environment

Lecture 4: Environment – Docker Installation

Lecture 5: Before Moving ahead major update

Lecture 6: Environment Setup – Installation of jenkins.

Lecture 7: Environment Setup – GitLab Installation

Lecture 8: Environment Setup – GitLab Password

Lecture 9: Introduction to Flask

Chapter 2: Part 1 Natural Language processing Programming

Lecture 1: Sentiment Analysis introduction and data set.

Lecture 2: Programming Python Flask Web API

Lecture 3: Sentiment Analysis Cleaning of data.

Lecture 4: Regex to remove username

Lecture 5: Punctutaion and body length Features

Lecture 6: Vectorizers and Model Selection

Lecture 7: HyperParameter tuning and model selection

Lecture 8: Major Course Update

Chapter 3: Part 2 Programming Python Flask NLP Model

Lecture 1: Understanding Templates and WebPages

Lecture 2: Importing webpages and main function

Lecture 3: Running our flask API

Chapter 4: Part 3 Introduction to docker commands and Dockerfile

Lecture 1: Understating the docker in Nutshell

Lecture 2: Docker brief CLI commands and tackling few errors

Lecture 3: Writing Dockerfile

Lecture 4: Updated Dockerfile

Lecture 5: GitHub Clone and docker build

Chapter 5: CI -CD Pipeline and jenkins configuration

Lecture 1: Course update before you start.

Lecture 2: Push code to GitLab and make Jenkins freestyle project

Lecture 3: WinSCP to Copy your local file to remote.

Lecture 4: Making Jenkinsfile and creating Jenkins pipeline

Lecture 5: Configuring CI-CD Pipeline with GitLab webhook and Jenkins

Chapter 6: Course Completion

Lecture 1: Congratulation for your completion

Instructors

Rating Distribution

  • 1 stars: 5 votes
  • 2 stars: 13 votes
  • 3 stars: 31 votes
  • 4 stars: 64 votes
  • 5 stars: 48 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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