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

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Mohammed Rijwan

4:56:00

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01 - Installation and Configuration

001 Introduction-Table Content.mp4

06:28

002 1.Environment-Virtual-Box-Configuration-and-Installation.pdf

002 Environment - Part 1) Virtual Box Configuration and Installation.mp4

06:33

003 Environment -Part ) Putty setup in virtual environment.mp4

03:00

004 2.Environment-Docker-Installation.pdf

004 Environment - Docker Installation.mp4

04:13

005 Before Moving ahead major update.mp4

02:39

006 Environment Setup - Installation of jenkins.mp4

07:49

007 4.Environment-Setup-GitLab-Installation.pdf

007 Environment Setup - GitLab Installation.mp4

18:46

008 Environment Setup - GitLab Password.mp4

04:26

009 Introduction to Flask.mp4

06:20

009 Some-details-of-Jenkins-GitLab-and-Flask.pdf

02 - Part 1 Natural Language processing Programming

001 Jupyter-notebook-and-Source-GitHub-Repository.pdf

001 Sentiment Analysis introduction and data set.mp4

01:46

002 1.Programming-Python-Flask-Web-API.pdf

002 Programming Python Flask Web API.mp4

10:33

003 Sentiment Analysis Cleaning of data.mp4

10:49

004 Regex to remove username.mp4

03:18

005 Punctutaion and body length Features.mp4

14:42

006 Count-Vectorizer-vs-TfIDF.pdf

006 Vectorizers and Model Selection.mp4

15:33

007 Confusion-Matrix.pdf

007 HyperParameter tuning and model selection.mp4

15:23

008 Major Course Update.mp4

11:21

03 - Part 2 Programming Python Flask NLP Model

001 Understanding Templates and WebPages.mp4

08:59

002 Importing webpages and main function.mp4

10:03

003 Running our flask API.mp4

03:27

003 WSGI-Server-More-informations.pdf

04 - Part 3 Introduction to docker commands and Dockerfile

001 Understating the docker in Nutshell.mp4

13:36

002 Docker brief CLI commands and tackling few errors.mp4

32:50

003 Writing Dockerfile.mp4

08:11

004 Updated Dockerfile.mp4

02:18

005 3.GitHub-Clone-and-docker-build-1.pdf

005 GitHub Clone and docker build.mp4

12:53

05 - CI -CD Pipeline and jenkins configuration

001 Course update before you start.mp4

18:41

002 1.Push-code-to-gitlab-and-make-Jenkinsfreestyle-project.pdf

002 Push code to GitLab and make Jenkins freestyle project.mp4

15:04

003 WinSCP to Copy your local file to remote.mp4

03:06

004 Making Jenkinsfile and creating Jenkins pipeline.mp4

13:18

005 Configuring CI-CD Pipeline with GitLab webhook and Jenkins.mp4

08:07

06 - Course Completion

001 Congratulation for your completion.mp4

01:48

Description

Python, Docker, Flask, GitLab, Jenkins tools and technology used for deploy model in your Local server. A complete Guide

What You'll 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 is this for?

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.

What You Need to Know?

Basic programming in any language

Some exposure to Python (but not mandatory)

More details

Description
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.
Who this course is for:
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.

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Hi, I am a working professional having experience in fields like ML, AI, DevOps tools like Docker, Jenkins, Git or GitLab, Python, C, C++, etc. I have worked in a much different organisation as a tech trainer and currently working as a Big Data and Machine Learning Engineer. My aim is to spread the knowledge which is an industrial standard and help you to find a better geek in you which will help you to make very robust projects which are highly in demand in the industries. I am sure my courses will help you to achieve your goals. Please visit my YouTube channel for more info. Cheer! Happy learning :-)

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