MDFT Academy/Automated Machine Learning with MLNET CLI

  • €95

Automated Machine Learning with MLNET CLI

In this training you'll learn how to conduct automated machine learning with the MLNET command line tool. The training will introduce you to the principles of automated machine learning and teach you the basics of regression and classification. You'll also discover how to use well-known machine learning algorithms like decision trees and ensembles to analyze your data.

Here's What You'll Get

169

Lessons

38

Videos

11

Quizzes

10

Projects

This training will get you fully up to speed with Microsoft’s MLNET command-line tool that can perform automated machine learning on any dataset.

You will learn how to use this tool on your own data to perform regression, binary and multiclass classification, clustering and recommendation. You’ll also learn how to use popular algorithms like gradient descent, the decision tree, bagging and boosting ensembles, k-means clustering and PCA-SVD.

As you progress through the training material, you’ll use the MLNET tool to auto-generate C# source code that will design, train, and evaluate machine learning models on your own computer. I will show you how the generated code works and how you can use it as the foundation of your own machine learning projects.

The training covers the following topics:

Supervised Machine Learning

Loading Numeric Data

Normalization

Processing Outliers

Processing Missing Values

Loading Text Data

One-Hot Encoding

Sparse Vector Encoding

Loading Geo Data

Binning

Vector Cross-Products

Single Linear Regression

RMSE, MSE and MAE

Gradient Descent

Multiple Linear Regression

Binary Classification

Accuracy, Precision and Recall

ROC, AUC and Bias

Multiclass Classification

The Confusion Matrix

Micro and Macro Average

Overfitting

Underfitting

Partitioning Datasets

Minibatch Training

K-Fold Cross Validation

Deep Neural Networks

Neural Network Architecture

Batch and Epoch Training

Decision Trees

Classification Trees

Regression Trees

Bagging Ensemble Models

Boosting Ensemble Models

Stacking Ensemble Models

Unsupervised Machine Learning

K-Means Clustering

The Davies-Bouldin Index

Recommendation Systems

Principal Component Analysis

Singular Value Decomposition

Lesson Preview

Check out this training lesson preview in which I will show you how neural networks are built on linear regression, a very simple machine learning algorithm that anyone can understand.

The video covers Deep Neural Networks, Activation Functions, and Iterative Training.

Featured Labs

Here are four lab assignments from the training. In each lab, you will be using the MLNET tool to train a machine learning model on your laptop using a well-known AI dataset.

Identify Objects In Images

Use an off-the-shelf multiclass classification model to identify everyday objects in images

Recommend Movies

Train a recommendation system that can suggest movies based on a dataset of ratings

Train a Spam Filter

Train a binary classification model that automatically recognizes and filters spam messages

Analyze the Titanic Disaster

Train a binary classification model to predict which passengers on the Titanic will survive the disaster

What You'll Need

For this course you will need a computer (running Windows, MacOS, or Linux), the NET SDK and the Microsoft MLNET command line tool.

Buy This Course

Choose the plan that works best for you. Buy this course and dive into automated machine learning, or unlock unlimited access to every course on the site.

Want the best learning experience?
Members get access to the full course library, all labs and community pages, receive priority support and sneak previews of future course releases.

Prices shown exclude VAT. EU businesses can defer VAT during checkout with a valid VAT ID number.

Buy This Course

Buy this course and get lifetime access to all lectures and knowledge quizzes

✔️ This course
✔️ Included quizzes only
✔️ Included labs only
🚫 No priority support
🚫 No community access
🚫 No future courses

€95 one time

Get A Membership

Get access to all courses, lectures, labs, quizzes, and future releases

✔️ All courses
✔️ All quizzes
✔️ All labs
✔️ Priority support
✔️ Community pages
✔️ Access to future courses

€35 p/mo or €350 p/yr

Team Training

In-company or online team training with guided labs and live support

🪙 Onsite or online
🪙 Conducted live
🪙 For teams of 5-12
🪙 3-day training
🪙 Guided labs
🪙 Can be customized

€1250 p/day

Training Curriculum

Course Introduction

I'm pleased to meet you!
Preview
Installing NET Core 3.0
Preview
Installing the ML.NET CLI
Installing Visual Studio Code
Recap
Next steps

Introduction To Machine Learning

In this section...
What is machine learning?
Preview
Supervised learning
Preview
Unsupervised learning
Recent developments
Recap
Next steps

Test Your Knowledge

In this section...
Quiz
Quiz review
Recap
Next steps

Loading And Processing Data

Introduction
In this section...
Introducing numeric data
Preview
Loading numeric data
Introducing string data
Preview
Loading string data
Introducing geo data
Loading Geo data
Loading text data
Test your knowledge
Quiz
Quiz review
App #1: Predict house prices in California
Recap
Next steps

Supervised Learning

Introducing supervised learning
Supervised learning

Regression

Introduction
In this section...
Introducing linear regression
Single linear regression
Introducing regression metrics
RMSE, MSE, and MAE
Introducing gradient descent
Gradient descent
Introducing multiple linear regression
Multiple linear regression
Test your knowledge
Quiz
Quiz review
App #2: Predict taxi fares in New York
Recap
Next steps

Binary Classification

Introduction
In this section...
Introducing binary classification
Binary classification
Introducing binary metrics
Accuracy, Precision, and Recall
Introducing ROC and AUC
ROC, AUC, and Bias
Test your knowledge
Quiz
Quiz review
App #3: Predict heart disease in Cleveland
Recap
Next steps

Multiclass Classification

Introduction
In this section...
Introducing multiclass classification
Multiclass classification
Introducing multiclass metrics
The confusion matrix
Micro and macro averages
Test your knowledge
Quiz
Quiz review
App #4: Recognize handwritten digits
Recap
Next steps

Training And Evaluating Models

Introduction
In this section...
Introducing overfitting
Overfitting
Introducing partitioning
Partitioning datasets
Minibatch training
Introducing K-fold cross validation
K-Fold Cross Validation
Test your knowledge
Quiz
Quiz review
App #5: Detect spam messages
Recap
Next steps

Deep Neural Networks

In this section...
Introducing deep neural networks
From linear regression to neural networks
The architecture of deep neural networks
How to visualize hidden network layers
How to train deep neural networks
Test your knowledge
Quiz
Quiz review
App #6: Recognize objects in images
Recap
Next steps

Decision Trees

Introduction
In this section...
Introducing classification trees
Classification trees
Introducing regression trees
Regression trees
Test your knowledge
Quiz
Quiz review
App #7: Predict Titanic survivors
Recap
Next steps

Ensemble Models

Introduction
In this section...
Introducing ensemble models
Ensemble models
Introducing bagging
Bagging
Introducing boosting
Boosting
Introducing stacking
Stacking
Test your knowledge
Quiz
Quiz review
App #8: Predict bike demand in Washington DC
Recap
Next steps

Unsupervised Learning

Introducing unsupervised learning
Unsupervised learning

Clustering

Introduction
In this section...
Introducing clustering
K-Means Clustering
Introducing clustering metrics
The Davies Bouldin Index
Test your knowledge
Quiz
Quiz review
App #9: Classify unlabeled Iris flowers
Recap
Next steps

Recommendation Systems

Introduction
In this section...
The challenge
Introducing PCA
PCA
Introducing SVD
SVD
Test your knowledge
Quiz
Quiz review
App #10: Recommend movies to me
Recap
Next steps

Test Your Knowledge (Again)

In this section...
Quiz
Quiz review
Recap
Next steps

In Conclusion

Course recap
What you've learned
Get your certificate of completion
Closing words

What My Students Are Saying

I would like to thank you very much for the great lessons you have given us. The instructions and lessons are very clear and personally I was very happy to have lessons from you. Very valuable. Thank you again.

Marc van Waes

Today I have seen two presentations from you and I have to say that I have become much wiser in these two hours. The speed and level of depth were exactly what I was hoping for. Thank you very much.

Eddy Kleinjan

Still Got Questions?

I hope I've given you a clear overview of the contents of this training course. But if anything is still unclear and you have some unanswered questions, then please check out this FAQ section

What is an online training?

In an online training you can study the training lectures and work on the homework assignments in your own time and at your own pace. You can spend as many or as little hours per week as you want on the training.

What's included?

You will receive prerecorded online video lectures, text lectures, multiple-choice quizzes and homework exercises.

How am I supported?

You are supported through email, and you can also book a 30-minute video support call with me if you want. I will help you with the training lectures and homework projects and get you ready for your certification exams.

Who should attend?

An online training is ideal for tech professionals who want to set their own learning pace and prefer to work independently with a bit of guidance and support throughout the training.

Where is the training hosted?

I host all my training content on Podia, a well-known e-learning platform based in the United States.

How long do I have access?

You have unlimited access to the online training content and your login account will never expire.

Can you train my entire team?

Yes! I often host classroom trainings where I teach tech subjects to an entire business team. Contact me and we'll get it organised.

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