Machine Learning with Azure
What is Machine Learning?
Important Concepts in Machine Learning
Machine Learning Algorithms
What is Data Science - Part 1
What is Data Science - Part 2
Azure Data Science
Quiz: What is Machine Learning?
About Azure Machine Learning
Using Azure Machine Learning Studio
Working with Modules in ML Studio
Working with Data in ML Studio
Demonstration: Download a Dataset and Summarize
Exercise: Sign into Azure ML Studio and Explore
Exercise: Import Data from a Local File
Exercise: Download Dataset from HTTP and Summarize.
Machine Learning Modules
How to Choose a ML Algorithm
Demonstrate: Predict Automobile Price - Part 1
Demonstrate: Predict Automobile Price - Part 2
Exercise: Create Predictive Model for Predicting Price of an Automobile
Create a Movie Recommender - Import and Clean Data
Create a Movie Recommender - Split Data and Train Model
Predict Ratings for Users Using the Movie Recommender
Generate Recommendations for Users
Exercise: Train a Movie Recommendation Engine
This course begins with explaining the need of Machine Learning and how it originated from Aritificial Intelligence and gave rise to deep learning. We explain important concepts in ML including categories of algorithms, statistical and computer science terms used in model creation, feature engineering, overfitting, generalization, underfitting and cross validation. We also dive into the topic of data science and discuss why ML is an important part of data science.
The course then provides hands on training on Azure Machine Learning, giving a tour of ML Studio, its various features and the concept of an experiment. We demonstrate the process of creating ML experiments and create predictive models to predice automobile prices and generate recommendations for movies.
The exercises in this course allow the student to get familiar with Azure Machine Leaning and gain confidence in exploring the tool further.
This guide presents the slides used in the course "Machine Learning with Azure".