Introducing ML.NET For Absolute Beginners - Part 1
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This document introduces ML.NET, an open-source machine learning framework for building custom machine learning models. It discusses why machine learning is important, with examples like personalized recommendations and data mining. Key applications of ML.NET are mentioned, such as sentiment analysis, product recommendation, and fraud detection. The document also provides an overview of machine learning techniques like clustering, regression, and classification and the four stages of building models with ML.NET: initializing the model, training it, scoring, and evaluating.
Introducing ML.NET For Absolute Beginners - Part 1
- 1. Introducing ML.NET For Absolute Beginners By Muhmmad Bilal Amjad Microsoft Most Valuable Professional
- 2. Machine Learning • Herbert Alexander Simon : “Learning is any process by which a system improves performance from experience. • Machine Learning is concerned with computer programs that automatically improve their performance through experience.
- 3. Why Machine Learning 1. Develop Systems that can automatically adapt and customize themselves to individual users. Example: Personalized Results, suggestions etc. 2. Data Mining: Discover new knowledge from large databases. 3. Ability to mimic human and replace certain monotonous tasks that require some intelligence. • What are your suggestions?
- 4. Why Machine Learning is the future? • Data is the new asset. • Flood of available data. • Increasing computational power. • Increasing support and demand from industries.
- 5. Some ML Applications 1. Image Processing 2. Computer Vision 3. Search Engine 4. Diagnosis 5. Speech Recognition 6. E-Commerce 7. Marketing 8. Personalization
- 7. Your Platform for building anything
- 8. What is ML.NET? 1. Machine Learning Framework for building custom ML Models. 2. Custom ML Made Easy. 3. Cross Platform 4. Open Source.
- 9. Applications of ML.NET 1. Sentiment Analysis 2. Product Recommendation 3. Price Prediction 4. Customer Segmentation 5. Fraud Detection 6. Spam Detection 7. Image Classification 8. Sales Forecasting
- 10. Machine Learning Modules. 1. Clustering Algorithm that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. 2. Regression Algorithm use for prediction. 3. Classification Algorithm to draw some conclusion from observed values. 4. Anomaly Algorithm that identify items or events that do not conform to an expected pattern or to other items present in a dataset.
- 11. 4 Stages of ML.NET 1. Initialize the model. Selecting the Best Fit Algorithm. 2. Train the model. Training is the process of analyzing input data by model. 3. Scoring. Score generates the results based on the trained model. It must have same info/column as of training model. 4. Evaluate. Trained Model will be compared with the test data to compare and predict the final results to be produced.
Editor's Notes
- #3: Herbert- Cognitive Psychologist