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30-Day Money-Back Guarantee
Development Data Science Docker

Deploy Machine Learning & NLP Models with Dockers (DevOps)

Learn to build Machine Learning, Deep Learning & NLP Models & Deploy them with Docker Containers (DevOps) (in Python)
Rating: 4.2 out of 54.2 (813 ratings)
4,467 students
Created by UNP United Network of Professionals
Last updated 6/2018
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • How to synchronize the versatility of DevOps & Machine Learning
  • Master Docker , Docker Files, Docker Applications & Docker Containers (DevOps)
  • Flask Basics & Application Program Interface (API)
  • Build & Deploy a Random Forest Model
  • Build a Text based (Natural Language Processing : NLP ) CLUSTERING (KMeans) Model and expose it as an API
  • Build an API which will run a Deep Learning Model (Convolutional Neural Network : CNN) Model for Image Recognition & Classification
Curated for the Udemy for Business collection

Course content

8 sections • 54 lectures • 4h 12m total length

  • Preview01:06
  • Preview01:04
  • Skills Checklist
    Preview01:32
  • Preview00:38

  • Preview01:31
  • Preview02:11
  • Preview02:43
  • Preview00:59
  • Preview00:25

  • Preview00:38
  • Setting up a Flask Project
    01:39
  • Simple Flask API to add two numbers
    04:25
  • Taking user input with GET requests
    04:13
  • POST request with Flask
    05:49
  • Using Flask in the context of Machine Learning
    03:26

  • Preview00:38
  • API & Dataset Overview
    01:05
  • Training the Random Forest model
    05:29
  • Pickling the Random Forest model
    02:39
  • Exposing the Random Forest model as a Flask API
    05:48
  • Testing the API model
    04:24
  • Providing file input to Flask API
    07:25
  • Flasgger for autogenerating UI
    08:33
  • Preview01:38

  • Preview00:46
  • Base Image & FROM command
    03:26
  • COPY and EXPOSE commands
    03:30
  • WORKDIR, RUN and CMD commands
    04:08
  • Preparing the flask scripts for dockerizing
    02:03
  • Writing the Dockerfile
    08:35
  • Building the docker image
    08:33
  • Running the Random Forest model on Docker
    08:52

  • Preview04:43
  • Overall Architecture
    04:21
  • Configuring the WSGI file
    08:05
  • Writing a production grade Dockerfile
    07:05
  • Running and debugging a docker container in production
    10:13
  • Docker Quiz 1 – Basic Concepts, Commands
    10 questions

  • Preview02:07
  • Stemming & Lemmatization for cleaner text
    10:34
  • Converting unstructured to structured data
    09:44
  • KMeans Clustering
    08:34
  • Preparing the excel output
    10:01
  • Making the output Downloadable
    06:18
  • Finding top keywords for kmeans clusters
    07:09
  • Final output with charts
    07:05
  • Preview01:27
  • Dockerizing the text clustering app
    2 questions

  • Preview00:48
  • Visualizing the input images
    07:14
  • Preparing the input images
    09:20
  • Building the deep learning model
    10:31
  • Training and saving the trained deep learning model
    03:01
  • Generating test images
    04:08
  • Flask API wrapper for making predictions
    08:09
  • Preview02:10
  • Dockerizing the deep learning app
    2 questions

Requirements

  • Basic programming in any language
  • Basic Mathematics
  • Some exposure to Python (but not mandatory)

Description

Machine Learning, as we know it is the new buzz word in the industry today. This is practiced in every sector of business imaginable to provide data-driven solutions to complex business problems. This poses the challenge of deploying the solution, built by the Machine Learning technique so that it can be used across the intended Business Unit and not operated in silos.

This is an extensive and well-thought course created & designed by UNP's elite team of Data Scientists from around the world to focus on the challenges that are being faced by Data Scientists and Computational Solution Architects across the industry  which is summarized the below  sentence :

"I HAVE THE MACHINE LEARNING MODEL, IT IS WORKING AS EXPECTED !! NOW, WHAT ?????" 

This course will help you create a solid foundation of the essential topics of data science along with a solid foundation of deploying those created solutions through Docker containers which eventually will expose your model as a service (API) which can be used by all who wish for it.

At the end of this course, you will be able to:

  • Learn about Docker, Docker Files, Docker Containers

  • Learn Flask Basics & Application Program Interface (API)

  • Build a Random Forest Model and deploy it.

  • Build a Natural Language Processing based Test Clustering Model (K-Means) and visualize it.

  • Build an API for Image Processing and Recognition with a Deep Learning Model under the hood (Convolutional Neural Network: CNN)

 This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications and most importantly deploying them.

Who this course is for:

  • Anyone willing to venture into the realm of data science
  • Anyone who would be interested in deploying a Data Science Solution, can be Regression, NLP or even Deep Learning Models

Instructor

UNP United Network of Professionals
Publishing top-notch data science learning materials
UNP United Network of Professionals
  • 4.3 Instructor Rating
  • 1,228 Reviews
  • 23,308 Students
  • 3 Courses

At UNP our vision is to make learning fun, fulfilling and personalized. We are working towards democratizing data science and breaking down the entry barrier to analytics and data science world.

We are committed to develop and publish top-notch data science learning materials. The materials are designed to make the students ready for the data science industry. All the contents developed at UNP are digital, either as e-books, video lectures, VR classrooms. Apart from distributing contents to individuals, we provide support for learning materials for corporate clients.

The learning materials are developed only by experienced data science professionals and professors from tier 1 universities. Every material goes through strict review procedure before it gets published. Every material coming out from UNP is accompanied by code snippets, application to industrial projects and tips to prepare for a job interviews.

Aligned with our vision, UNP scholarship program is set to provides learning opportunities for students with financial challenges.

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