How to Become A Data Scientist Using Azure Machine Learning
How to Become A Data Scientist Using Azure Machine Learning, available at $19.99, has an average rating of 3.85, with 39 lectures, 5 quizzes, based on 231 reviews, and has 1192 subscribers.
You will learn about Build an end to end Predictive Model In Azure Machine Learning Studio You'll gain a high level background in data science. You'll be able to effectively use Microsoft's AML service. This course is ideal for individuals who are This course is for developers, business analysts and any data professional who want to learn the foundation of data mining. It is particularly useful for This course is for developers, business analysts and any data professional who want to learn the foundation of data mining.
Enroll now: How to Become A Data Scientist Using Azure Machine Learning
Summary
Title: How to Become A Data Scientist Using Azure Machine Learning
Price: $19.99
Average Rating: 3.85
Number of Lectures: 39
Number of Quizzes: 5
Number of Published Lectures: 39
Number of Published Quizzes: 5
Number of Curriculum Items: 44
Number of Published Curriculum Objects: 44
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Build an end to end Predictive Model In Azure Machine Learning Studio
- You'll gain a high level background in data science.
- You'll be able to effectively use Microsoft's AML service.
Who Should Attend
- This course is for developers, business analysts and any data professional who want to learn the foundation of data mining.
Target Audiences
- This course is for developers, business analysts and any data professional who want to learn the foundation of data mining.
There can be little doubt that the single hottest careerin the data field is the data scientist or BI developer skilled in predictive analytics.
Yes, Big Data is on everyone’s lips but what happens after that big data is ingested into a data lake?
The answer is predictive analytics.
Because we live in the big data era, machine learning has become much more popular in the last few years.
Having lots of datato work with in many different areas lets the techniques of machine learning be applied to a broader set of problems.
Data can hold secrets, especially if you have lots of it.
With lots of data about something, you can examine that data in intelligent ways to find patterns.
This is exactly what machine learning does: It examines large amounts of data lookingfor patterns, then generates code that lets you recognize those patterns in new data.
Your applicationscan use this generated code to make better predictions. In other words, machine learning can help you create smarter applications.
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.
In this course you will be learning and building predictive algorithms using Azure Machine Learning Studio.
At the end of this course you’ll be able to build and evaluate a binary classification predictive model without authoring a single line of code
You’ll build an Experiment for a targeted email campaigned and be able to tell what customers should receive flyers and those that shouldn’t.
Thanks for reading about Azure Machine Learning Studioand I’ll see you in the course.
Course Curriculum
Chapter 1: An Introduction to Data Science
Lecture 1: Course Introduction
Lecture 2: Is this Course Right For You?
Lecture 3: Download Course Material Here
Lecture 4: What is Data Science?
Lecture 5: Analytics Spectrum
Lecture 6: Why Use Azure Machine Learning Studio?
Lecture 7: Why Does It Matter Now?
Lecture 8: The High Level Data Science Process
Lecture 9: Azure Algorithms
Lecture 10: Terminology
Lecture 11: Summary
Chapter 2: Azure Machine Learning
Lecture 1: Azure Machine Learning Studio
Lecture 2: Components of an Experiment
Lecture 3: Four Step Creation Process
Lecture 4: Confusion Matrix Overview
Lecture 5: Terminology
Lecture 6: Summary
Chapter 3: Introduction to Statistical and Machine Learning Algorithms
Lecture 1: Machine Learning and Supervision
Lecture 2: Anomaly Detection
Lecture 3: Classification
Lecture 4: Terminology
Lecture 5: Summary
Chapter 4: Creating A Simple Binary Classification Model
Lecture 1: Use Case
Lecture 2: Why A Binary Classification Model?
Lecture 3: Creating The Experiment – Part 1
Lecture 4: Creating The Experiment – Part 2
Lecture 5: Add a Second Algorithm to Compare Against Our First
Lecture 6: Reading The Models Outcome
Lecture 7: Terminology
Lecture 8: Summary
Chapter 5: Building A Simple Targeted Marketing Campaign
Lecture 1: The Business Use Case
Lecture 2: Export The Dataset
Lecture 3: Upload Data Set
Lecture 4: Creating the Experiment – Part 1
Lecture 5: Creating the Experiment – Part 2
Lecture 6: Analyze the Outcome
Lecture 7: Scoring Our Model
Lecture 8: Summary
Chapter 6: Conclusion
Lecture 1: Conclusion
Instructors
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Mike West
Creator of LogikBot
Rating Distribution
- 1 stars: 10 votes
- 2 stars: 18 votes
- 3 stars: 49 votes
- 4 stars: 94 votes
- 5 stars: 60 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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