Machine Learning With ML.NET
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Sample Codes: https://github.com/davegautam/dotnetconfsamplecodes Presentation on How you can get started with ML.NET. If you are existing .NET Stack Developer and Wanna use the same technology into Machine Learning, this slide focuses on how you can use ML.NET for Machine Learning.
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Machine Learning With ML.NET
- 1. Machine Learning With ML.NET Getting Started With Machine Learning for .NET Stack Developers.
- 2. I love writing reusable components, solving technical problems for team & Designing Architecture of solutions along with managing projects in scrum. Senior Project Manager Braindigit IT Solutions Application & Database Specialist Nutrition Innovation Lab (USAID Project) Senior Software Engineer Bitscrafters INC Software Engineer Softech Infosys Education MSC IT (Data Science) Masters in Business Administration Bachelors of Computer Application
- 3. Applying Machine Learning? Needed for tasks that are too complex for humans to code directly.
- 5. Have a Problem That Needs ML? • Classify Given Input into A or B or C • Classification & Multi-Class Classification • Which Marketing Campaign Brought More Customers : Win Gold or Win Lunch Coupon • Anomaly detection flags unexpected or unusual events or behaviors • Fraud Detection of Credit Card • Mail Spam Detection • Make Numerical Predictions • Predict Sales of Next Quarter • Predict Whiskey Sales Looking at Temperature
- 6. Have a Problem That Needs ML? • Understand the Structure of Data • Clustering Algorithms • Which Age Group Like Same Type Series • Learn From Outcome & Decide on Other Actions • Self Driving Car: At a yellow light, brake or accelerate?
- 7. Do you have the Data? • Relevancy of Your Data Against your Problem. • You Need to Find Life Expectancy%, You have series of data about their expenses in buying Vegetables. (Irrelevant) • You Need to Find Life Expectancy%, You have series of data about their expenses in buying Vegetables & also you know if they were likely TOXIC. (Relevant) • Do you Have Enough Data? • It Depends; No One Can Tell You • MORE DATA ALWAYS BETTER • You can determine Enough data using, Statistical Heuristic, Dataset Size vs Model Skill, Domain Expertise, Analogy & Domain Expertise
- 8. Do you have the Data? • Accuracy of the Data • Highly Concentrated Wrong Data is Problem • Accuracy of Data is itself a first ask since Machine Learning are Predictions, and Predictions with wrong data would be Highly Incorrect. • Is your Data Connected? • Significant Amount Of Missing Data would hamper your ML • You Should have Connected Data.
- 9. Introduction To ML.NET Microsoft recently open sourced its machine learning framework that is available on GitHub. ML.NET is an open source cross-platform for machine learning for .NET framework.
- 10. Introduction To ML.NET • Originally developed in Microsoft, used in Windows, Bing, Azure, and more • The idea is to help .NET developers get in on cutting-edge ML programming without having to learn the underlying technical details associated with creating and tuning machine learning models. • Cross Platform- runs on any platform where 64 bit .NET Core or later is available • Open Source • Licensed Under MIT can be found in GitHub.
- 11. ML.NET Capabilities ML Tasks • Classification (e.g. text categorization and sentiment analysis) • Regression (e.g. forecasting and price prediction) • Clustering Training Models • .NET APIs for training models, using models for predictions • Core components of this framework, such as learning algorithms, transforms, and core ML data structures Extensions or Integration • Integration with Python
- 13. Future RoadMap • Additional ML Tasks and Scenarios • Deep Learning with TensorFlow (Already Integrated in ML.NET 0.5) & CNTK • ONNX support • Scale-out on Azure • Better GUI to simplify ML tasks • Integration with VS Tools for AI • Language Innovation for .NET • CNTK, Accord.NET, TensorFlow integration with one single API
- 14. Let’s Apply Some Machine Learning • Ask Question That has an Answer (Exact Answer) • Apply the Right Method to Find the Answer
- 15. Problem: Taxi Fare Prediction • Problem • Predicting the fare of a taxi trip in New York City • Statistical Inferencing • regression analysis is a set of statistical processes for estimating the relationships among variables. WIKI. • (y=ax+b), many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors').
- 16. Solution: Taxi Fare Prediction Load & Transform Data •Pipeline-Workflow to Train Your Data •TextLoader- Load CSV Data •ColumnCopier- Predict Values are Copied to Specified Column •CategoricalOneHotVectorizer- transform the categorical data into numeric values •ColumnConcatenator-combines all of the feature columns into the Features Choosing Learning Algorithm & Training Model • Regression- FastTreeRegressor learner utilizes gradient boosting. Gradient boosting is a machine learning technique for regression problems. It builds each regression tree in a step-wise fashion. It uses a pre-defined loss function to measure the error in each step and correct for it in the next. Evaluate Model • Process of checking how well the values are predicted • RMS- measure of the differences between values predicted by a model and the values observed. The lower it is the Better. • Rsquared- The Closer it is to 1 it’s better. Provides a measure of how well observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model
- 17. Demo
- 18. Problem: Sentiment Analysis • Problem • Predict the sentiment of a new website comment, either positive or negative • Statistical Inferencing • Classification • Binary or binomial classification is the task of classifying the elements of a given set into two groups (predicting which group each one belongs to) on the basis of a classification rule.
- 19. Solution: Sentiment Analysis Load & Transform Data •Pipeline-Workflow to Train Your Data •TextLoader-convert the SentimentText column into a numeric vector Choosing Learning Algorithm & Training Model • Binary Classification- FastTreeBinaryClassifier, Gradient boosting is a machine learning technique for regression problems, In case of a binary classification problem, the output is converted to a probability by using some form of calibration. Evaluate Model • Process of checking how well the values are predicted • Computes the quality metrics for the PredictionModel using the specified data set.
- 20. Demo
- 22. Making Data Right • Get More Quality Data • Generate More Data • Data Cleaning • Reframing Problem • Transform Your Data :Gaussian • Select Your Features Right • Engineer Your Features
- 23. Implying the Algorithm Right • Resampling: Use a method and configuration that makes the best use of available data. The k-fold cross-validation method with a hold out validation dataset might be a best practice. • Evaluation Metric. What metric is used to evaluate the skill of predictions? Use a metric that best captures the requirements of the problem and the domain • Baseline Performance. What is the baseline performance for comparing algorithms? Use a random algorithm or a zero rule algorithm (predict mean or mode) to establish a baseline by which to rank all evaluated algorithms. • Spot Check Linear Algorithms • Spot Check Nonlinear Algorithms. • Steal from Literature. What algorithms are reported in the literature • Standard Configurations • Try Alternatives
- 24. This is how it would look like.