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
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 tocomplex business problems. This posesthe challenge of deploying the solution,built by the MachineLearning technique so that it can be used across the intendedBusinessUnit and not operated in silos.
This is an extensive and well-thoughtcourse created & designedby UNP's elite team of Data Scientists from around the worldto 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 HAVETHEMACHINELEARNINGMODEL, ITISWORKINGASEXPECTED !! NOW,WHAT ?????"
This course will help you create a solid foundation of the essential topics ofdata science along witha 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, DockerContainers
Learn Flask Basics &Application Program Interface (API)
BuildaRandom Forest Model and deploy it.
Build a Natural Language Processing basedTest Clustering Model (K-Means) and visualize it.
Build an APIforImage Processing and Recognition with a Deep Learning Model under the hood (Convolutional Neural Network: CNN)
This course is a perfect blend of foundationsof data science, industry standards, broader understanding of machine learning and practical applications and most importantly deploying them.
