|Application fee:||15.5 USD|
THE OBJECTIVE OF THIS COURSE:
The aim of this course is to fasten the creation of ML Models and put them as a Service to the Clients.
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems?
Every day you see more and more examples of machine learning in your life. In this course, Getting Started with Azure Machine Learning, you will learn how to develop and deploy predictive solutions using Azure Machine Learning. First, you will see how, with a little dragging and dropping, you can create solutions from scratch. Next, if you already have a solution implemented in R or Python, you will learn how to scale them up with Azure Machine Learning. Finally, you'll end the course by learning about how to maintain your Azure Machine Learning solution. After finishing this course, you'll have gone from a machine learning novice to having a prediction solution service ready to integrate into your applications to make them smarter and more useful.
Machine learning helps predict the weather, route you around traffic jams, and display personalized ads on your web pages. In this course, you will learn how to use Azure machine learning in order to create, deploy, and maintain predictive solutions.
This course focuses on 2 aspects:
1. Learning to use MS Azure Studio to build models, validate and compare them at a very fast pace.
2. MLAAS – machine learning as a service which focuses on deploying the models as a web service (basically as a product).
Why with Python???
Azure Machine Learning (Azure ML) is a cloud service that helps people execute the machine learning process.
As its name suggests, it runs on Microsoft Azure, a public cloud platform.
Because of this, Azure ML can work with very large amounts of data and be accessed from anywhere in the world. Using it requires just a web browser and an internet connection.
Teaching Methodology consists of theory and practical with interactive discussions on every topic. Presentation and board teaching on smart board make it easy to understand for the candidate. After class, assessment would be in the form MCQs, assignments etc.
Basics of Statistics and Probability, Basic knowledge of Machine Learning with R/Python
Explore the data science process – An Introduction
Probability and statistics in data science
Working with data – Ingestion and preparation
Data Exploration and Visualization
Introduction to Supervised Machine Learning
Microsoft Azure Machine Learning Studio https://studio.azureml.net/
(No need to download)