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
Basic programming in any language
Some exposure to Python (but not mandatory)
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
Course Size Details :
4 hours on-demand video
10 downloadable resources
Full lifetime access
Access on mobile and TV
Certificate of completion
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