Installing NET Core 3.0

Installing NET Core 3.0

In this course we're going to use NET Core version 3.0. This is a modern version of the NET Framework that runs on Windows, OS/X, and Linux.

This means that you can build the assignments in this course using either a Windows computer, a Mac, or a Linux machine. You do not need to install a special virtual machine to run the code.

We will build and run all applications directly from the command line. We'll need to install the NET Core Software Development Kit (SDK) for this.

You can download the SDK here:
https://dotnet.microsoft.com/download/dotnet-core/3.0

Make sure that you download the correct installer for your operating system:



Run the installer for your operating system to install the NET Core SDK. Then check if everything got installed ok by running the following command on the command line:

dotnet --info

You should see information about your operating system and the installed version of NET Core. Here's what I see when I run the command on my Windows laptop:




Automated Machine Learning with MLNET CLI

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Course Introduction

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

Introduction To Machine Learning

  • In this section...
  • What is machine learning?
  • Supervised learning
  • 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
  • Loading numeric data
  • Introducing string data
  • 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 Validation3
  • 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 Clustering1
  • 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