Machine Learning Made Easy - Data for Good Regina
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All companies want to use machine learning, but face many roadblocks to getting there. It can be hard for an organization to get the skills, technology and computing power necessary to build a working machine learning model, and deploy it as a pipeline. Modern Cloud providers have a host of tools to make machine learning easier than ever before and they have available computing power to back it up. In this learning focused session, Ryan will introduce you to some basics of data for machine learning and show how cloud services like Microsoft Azure Machine Learning have made building scalable and accurate Machine Learning pipelines as easy as pivoting a table in excel.
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Machine Learning Made Easy - Data for Good Regina
- 2. Welcome! Let’s get acquainted Experience Location Highlights Contact • https://www.linkedin.com/in/r yan-tessier-30864942
- 4. My Talk in a Nutshell
- 6. Part 1 Introduction to Machine Learning
- 7. Rosa Multiflora Rosa Multiflora Rosa Multiflora Tulipa Tulipa Bellis Perenis Bellis Perenis Bellis Perenis Tulipa Bellis Perenis
- 9. 56 115 61 126 67 137 72 140 76 152 82 156 0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 100 y (cycle rentals) x (Temperature) 54 114 62 129 68 130 74 152 80 154 ŷ 116 128 137 146 155 f(x) 0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 100 y (cycle rentals) x (Temperature) 35 1 1.5 x y
- 10. 0 0.5 1 y x [4,2,3.2,10] 0 [6,3,1.7,12] 1 [5,2,3.5,11] 0 [4,1,2.9,10] 0 [7,4,2.1,11] 1 x y [3,1,2.8,10] 0 [7,3,1.8,11] 1 [4,8,2.5,10] 0 [4,1,3,1,11] 1 0 0.5 1 y x P(1) P(0) ŷ 0.2 0.8 0 0.9 0.1 1 0.6 0.4 1 0.3 0.7 0 [4,2,3.2,10] [6,3,1.7,12] [5,2,3.5,11] [4,1,2.9,10] [7,4,2.1,11] [3,1,2.8,10] [7,3,1.8,11] [4,8,2.5,10] [4,1,3,1,11] Predicted Actual True Positive False Positive False Negative True Negative
- 15. Part 2 How the cloud supports machine learning
- 16. © Copyright Microsoft Corporation. All rights reserved.
- 17. ©Microsoft Corporation Azure Top 3 Cloud Computing Services Amazon Web Services Microsoft Azure Google Cloud
- 19. ©Microsoft Corporation Azure IaaS vs PaaS vs SaaS Software-as-a Service Platform as a Service Infrastructure as a Service
- 20. ©Microsoft Corporation Azure IaaS vs PaaS vs SaaS – How we make a cake… Software-as-a Service Platform as a Service Infrastructure as a Service
- 21. ©Microsoft Corporation Azure Major Cloud Provider Machine Learning Services Amazon SageMaker Azure Machine Learning Google Vertex AI
- 23. Part 3 AutoML and Azure Machine Learning
- 26. ml.azure.com
- 30. © Copyright Microsoft Corporation. All rights reserved.
- 31. © Copyright Microsoft Corporation. All rights reserved. • Dropping high-cardinality features • Imputing missing values • Categorical encoding • Derived feature generation
- 32. © Copyright Microsoft Corporation. All rights reserved.
- 33. Lets try it out! Azure Machine Learning
- 37. ©Microsoft Corporation Azure Overview We covered the following concepts: Introduction to machine learning • What is machine learning? • Regression • Classification Cloud Computing • Cloud Providers • IaaS vs PaaS vs SaaS Azure Machine Learning • What is Azure Machine Learning? • Automated Machine Learning
- 38. Questions?
- 39. ©Microsoft Corporation Azure Job Opportunity – Be a Data Scientist on my Team https://saskenergy.wd10.myworkdayjobs.com/en- US/SaskEnergy/job/Regina-SK/Data- Scientist_R00001435 New Team Exciting Projects Opportunities for Professional Development
- 40. ©Microsoft Corporation Azure Resources • Microsoft Learn • Azure Cloud Fundamentals AZ-900 • Azure AI Fundamentals AI-900 • Azure Data Scientist DP-100 • How to Select the Right Algorithm for Model Training • Kaggle.com
Editor's Notes
- This lesson covers some basic principles of machine learning. It's important for students to understand these principles to provide a context for the Azure services we'll explore later in the module. There's some basic mathematics involved, and some students may find this intimidating. Take it slowly and emphasize that the goal is not to teach linear algebra or probability theory – just to help students gain an "intuition" into how machine learning works so that the configuration and evaluation options in the Azure Machine Learning interface make sense.
- This is an animated slide – use the notes below to talk to each animation build (clicks are indicated by numbers) Machine Learning is a technique we can use to create predictive models based on relationships in data. So what does that mean? Well, let's look at an example: Suppose a botanist collects some samples of flowers. {The slide shows images of various flowers} Each sample has some features, such as measurements of the petals, stem, and other details); and the botanist can provide a label for the flower using its species name. {The Latin name for each flower species appears} The sample data is then processed using an algorithm that tries to find a relationship between the features and the label. {The flowers are fed into a pair of turning cogs} The result of this algorithm is a machine learning model. {A human head with cogs for a brain appears} And since the model has learned the relationship between the features and label, you can use it to predict the species of a flower based on its features. {A new flower appears, and the human head identifies its species}
- Extract Features – choosing features and manipulation Split dataset – Train and test dataset Train Model – Input training dataset into a machine learning model Validate – If desires performance isn’t reached: tune the model and repeat step 3 Deploy – package the model onto some kind of server or computer and have it available to make predictions.
- Extract Features – choosing features and manipulation Split dataset – Train and test dataset Train Model – Input training dataset into a machine learning model Validate – If desires performance isn’t reached: tune the model and repeat step 3 Deploy – package the model onto some kind of server or computer and have it available to make predictions.