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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