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)

Created by: UNP United Network of Professionals

Produced in 2018

What you will 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

Quality Score

Content Quality
Video Quality
Qualified Instructor
Course Pace
Course Depth & Coverage

Overall Score : 80 / 100

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Course 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 :
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

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Instructor Details

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.



48 total reviews

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Awesome course! To the point and for advanced user who wants to go the the next level. I would expect more examples as suggested by the instructor to try reading and writing on sql, or file etc - which will greatly enhance the course even further.

Videos are excellent understanding but having doubts which are not being cleared. If any help on the doubts it will be really helpful

Having some docker-specific error debugging in the lectures was a nice touch, but all the Python spelling errors wasted a lot of unnecessary time that could have been prevented with a little extra preparation

Concepts are presented in very clear and professional . I am really enjoying this course

This course fulfills exactly what it propose. Learned about flask framework, build machine learning using docker, build a WSGI, clustering & NLP processes to deploy. Great classes!

1/ Does not cover some dependencies such as usage of pip, at least in the order that you need them.2/ Explaination of GET and POST verbs is misleading.3/ Explaining why we want to perform an action with another question, such as "Why not build the classifier?", is not very informative.4/ Using colloquialism's such as "live life king size" and "bad boy" is not very informative.5/ Using "paint" instead of a prepared images seems poor.6/ The presenter is unprepared generally for the material that he is presenting.7/ Misses important parts related to the subject such as retraining models and how this is done.8/ Spends lots of time talking about the ML implementation, rather than the subject matter of deploying such models.Summary: Competitors do a much better job.

I have been wanting to learn this stuff for a long time, but never knew where to find it. I am so excited to try this out on my own projects! The instructor explains it so simply. Edit: This instructor does not appear to be responsive to questions in the Q&A section.

A very quick crash course that covers only the necessary topics in minimum possible time. The ML part is bare minimum. So, if you're thinking of learning ML part through this course, don't keep much expectations. As far as docker part is there, the instructor teaches the basics and develops a basic application, rest ML applications in course are left to dockerize ourselves as exercise.

The author explains very good and the course delivers all that is in the content. I was able to learn docker, although I had to pass through a lot of debugging to do the first homework. I learned a lot! Negative points: 1-the english legends have a lot of mistakes 2-there are forum questions that have not been answered.

The course is well explained. However, there are few bugs and the instructor does not answer questions.

I love the pace and the way this instructor explained the programming syntax, problem and resolution. My only challenge is he should have expanded the Editor all the way to the right and move the terminal console below or above it. That way, students can see all the codes that was written. I know that I can download the code from the resource file, but watching and seeing everything in one place is a trivial bonus. Other than that, I love the practicality of this course.

Vivek has elaborated the difference between Docker containers and VMs really well.