Machine Learning No-Code Approach: Using Azure ML Studio
Machine Learning No-Code Approach: Using Azure ML Studio, available at $79.99, has an average rating of 4.45, with 50 lectures, 2 quizzes, based on 566 reviews, and has 5776 subscribers.
You will learn about Examine the foundations of Supervised Machine Learning Use Azure ML Studio to create Predictive Models without code Evaluate different algorithms to find the one that works best Deploy models live to be used with new data Build a real estate model to predict house prices Experiment with the traditional Titanic Dataset to predict survival chances This course is ideal for individuals who are Technology Professionals or Curious about Machine Learning or Not a Coder It is particularly useful for Technology Professionals or Curious about Machine Learning or Not a Coder.
Enroll now: Machine Learning No-Code Approach: Using Azure ML Studio
Summary
Title: Machine Learning No-Code Approach: Using Azure ML Studio
Price: $79.99
Average Rating: 4.45
Number of Lectures: 50
Number of Quizzes: 2
Number of Published Lectures: 50
Number of Published Quizzes: 2
Number of Curriculum Items: 52
Number of Published Curriculum Objects: 52
Original Price: $99.99
Quality Status: approved
Status: Live
What You Will Learn
- Examine the foundations of Supervised Machine Learning
- Use Azure ML Studio to create Predictive Models without code
- Evaluate different algorithms to find the one that works best
- Deploy models live to be used with new data
- Build a real estate model to predict house prices
- Experiment with the traditional Titanic Dataset to predict survival chances
Who Should Attend
- Technology Professionals
- Curious about Machine Learning
- Not a Coder
Target Audiences
- Technology Professionals
- Curious about Machine Learning
- Not a Coder
Machine Learningis the most in demand technical skill in today’s business environment. Most of the time though it is reserved for professionals that know how to code.
But Microsoft Azure Machine Learning Studio changed that. It brings a drag-n-drop easy to use environment to anyone’s fingertips. Microsoft is known for its easy-of-use tools and Azure ML Studio is no different.
However, as easy as Azure ML Studio is, if you don’t know Machine Learning, at least the basics, you won’t be able to do much with the tool. This is one of the goals of this course: To give you the foundational understanding about Machine Learning. You will get the base knowledge required to not only talk proficiently about ML, but also to put it into action and execute on business needs.
We will go through all the steps necessary to put together a Supervised Learning prediction model, whether you need Classification (for discrete values like “Approved” or “Nor Approved”) or Regression (for continuous values like “Salary” or “Price”).
The course will only require you to have basic knowledge of math including the basic operations and how to calculate average. Some exposure to Microsoft Excel would be good as during deployment of the live model, we will be using Excel to perform demonstrations.
This course has been designed keeping in mind technologists with no coding background as we use a “no-code approach”. It is very hands-on, and you will be able to develop your own models while learning. We will cover:
– Basics of the main three main types of Machine Learning Algorithms
– Supervised Learning in depth
– Classification by using the Titanic Dataset
– Understanding and selecting the features from the dataset
– Changing the metadata of features to work better with ML Algorithms
– Splitting the data
– Selecting the Algorithm
– Training, scoring, and evaluating the model
– Regression by using the Melbourne Real Estate Dataset
– Cleaning missing data
– Stratifying the data
– Tuning hyperparameters
– Deploying the models to a Excel
– Providing web service details to developers in case you want to integrate with external systems
– Azure ML Cheat Sheet
The course also includes 4 assignments with solutions that will give you an extra chance to practice your newly acquired Machine Learning skills.
In the end you will be able to use your own datasets to help your company with data prediction or, if you just want to impress the boss, you will be able to show the new tool you have just added to your toolbelt.
If you are not a coder and thought there would be no place for you to ride the Machine Learning wave, think again. You can not only be part of it, but you can master it and become a Machine Learning hero with Azure ML Studio.
Enroll today and I will see you inside!
Course Curriculum
Chapter 1: Welcome to the Course
Lecture 1: Welcome
Lecture 2: Compare Machine Learning Categories
Lecture 3: Create a Free Azure Account
Lecture 4: Define Azure ML Studio Features
Chapter 2: Classification Using the Titanic Dataset
Lecture 1: Introduction
Lecture 2: Load the Dataset
Lecture 3: Understand the Features
Lecture 4: Select Features
Lecture 5: Edit Metadata
Lecture 6: Split the Data
Lecture 7: Select the Algorithm
Lecture 8: Train Model
Lecture 9: Score Model
Lecture 10: Evaluate Model
Lecture 11: Exercise 1: The Iris Flower
Lecture 12: Exercise 1: The Iris Flower (Solution)
Lecture 13: Summary
Chapter 3: Refining The Classification Model
Lecture 1: Introduction
Lecture 2: Summarize The Data
Lecture 3: Select More Features
Lecture 4: Clean Missing Data
Lecture 5: Stratify The Data
Lecture 6: Tune The Hyperparameters
Lecture 7: Evaluate Model in Depth
Lecture 8: Compare Different Algorithms
Lecture 9: Deploy The Model
Lecture 10: Exercise 2: Refining The Iris Flower
Lecture 11: Exercise 2: Refining The Iris Flower (Solution)
Lecture 12: Summary
Chapter 4: Regression Using A Real Estate Dataset
Lecture 1: Introduction
Lecture 2: Explore The Data
Lecture 3: Clean Missing Data
Lecture 4: Edit Metadata
Lecture 5: Test Model
Lecture 6: Evaluate Model
Lecture 7: Optional – MAE and RAE Explained
Lecture 8: Exercise 3: Iowa Housing Market
Lecture 9: Exercise 3: Iowa Housing Market (Solution)
Lecture 10: Summary
Chapter 5: Refining The Regression Model
Lecture 1: Introduction
Lecture 2: Reassess Feature Selection
Lecture 3: Hyperparameter Tuning
Lecture 4: Compare Algorithms
Lecture 5: Deploy The Model
Lecture 6: Exercise 4: Refining Iowa Housing Market
Lecture 7: Exercise 4: Refining Iowa Housing Market (Solution)
Lecture 8: Summary
Chapter 6: Conclusion
Lecture 1: What You Have Learned
Lecture 2: Next Steps
Lecture 3: Bonus Lecture
Instructors
-
Aderson Oliveira
Tech Instructor
Rating Distribution
- 1 stars: 2 votes
- 2 stars: 4 votes
- 3 stars: 32 votes
- 4 stars: 229 votes
- 5 stars: 299 votes
Frequently Asked Questions
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