marwan1023 / udacity-intro-to-ml-labs

Intro to Machine Learning labs

Azure Machine Learning designer gives you a cloud-based interactive, visual workspace that you can use to easily and quickly prep data, train and deploy machine learning models. It supports Azure Machine Learning compute, GPU or CPU. Azure Machine Learning designer also supports publishing models as web services on Azure Kubernetes Service that can easily be consumed by other applications. To use Azure Machine Learning designer, you do not need programming experience and this quickstart will walk you through an exercise that will show how to process training data, create a model, train, score, and evaluate the model and finally deploy the trained model as a web service.

Lesson 1

Concept 9 - The Models

Lab 1: Use an algorithm (linear regression) to train a model

Lesson 2

Concept 1 - Data Import and Transformation

Lab 2: Import, transform, and export data

Concept 2 - Manage Data

Lab 3: Create and version a dataset

Lab 4: Engineer and select features

Concept 3 - Model Training Basics

Lab 5: Train and evaluate a model

Concept 4 - Ensembles

Lab 6: Train a two-class decision forest

Lab 7: Train a simple classifier with Automated ML

Lesson 3

Concept 1 - Supervised Learning, Classification

Lab 8: Compare the performance of the various two-class classifiers

Lab 9: Compare the performance of the various multiclass classifiers

Concept 2 - Classifier using Automated Machine Learning

Lab 10: Train a classifier using automated machine learning

Concept 3 - Supervised Learning, Regression

Lab 11: Compare the performance of the various regressors

Concept 4 - Regression using Automated Machine Learning

Lab 12: Train a regressor using automated machine learning

Concept 6 - CLustering

Lab 13: Train a simple clustering model

Lesson 4

Concept 1 - A taste of deep learning

Lab 14: Classical ML vs. Deep Learning: multiclass neural net module

Concept 3 - Similarity learning recommendation

Lab 15: Train a simple recommender

Concept 4 - Text classification

Lab 16: Train a simple text classifier

Concept 7 - Forecasting

Lab 17: Train a time-series forecasting model using automated machine learning

Lesson 5

Concept 2 - Compute Resources

Lab 18: Managing compute

Lab 19: Train a machine learning model from a managed notebook environment

Concept 3 - Basic Modeling

Lab 20: Explore experiments and runs

Concept 5 - Operationalizing Models

Lab 21: Deploy a trained model as a webservice

Concept 6 - Programatically Accessing Managed Services

Lab 22: Training and deploying a model from a notebook running in a Compute Instance

Lesson 6

Concept 2 - Model transparency and explainability

Lab 23: Explore model explanations