FreeCoursesOnline Me PacktPub - Hands-On Machine Learning for dotNET Developers

1. 1.Finding the Best Price on Laptops Using Price Prediction (Regression)/01.The Course Overview.mp49.43MB
2. 1.Finding the Best Price on Laptops Using Price Prediction (Regression)/02.Demo of the Application and How to Apply Machine Learning.mp412.28MB
3. 1.Finding the Best Price on Laptops Using Price Prediction (Regression)/03.Installing the ML.NET Model Builder.mp46.75MB
4. 1.Finding the Best Price on Laptops Using Price Prediction (Regression)/04.Automatically Generate a Model with the ML.NET Model Builder.mp47.55MB
5. 1.Finding the Best Price on Laptops Using Price Prediction (Regression)/05.Using the Final Model in the Desktop Application.mp416.03MB
6. 1.Finding the Best Price on Laptops Using Price Prediction (Regression)/06.Generating the Model Using the ML.NET CLI Tool.mp46.99MB
7. 2.Determining Aggression in User Comments/07.Demo of the Web API and the Wikipedia Aggression Dataset.mp46.93MB
8. 2.Determining Aggression in User Comments/08.Digging into the Code Learn What a Training Pipeline Is.mp414.72MB
9. 2.Determining Aggression in User Comments/09.Implementing a Pipeline for the Aggression Scorer.mp417.93MB
10. 2.Determining Aggression in User Comments/10.Using the Custom Model in the Web API.mp421.56MB
11. 3.Evaluating, Improving, and Retraining Your Model/11.Evaluating Your Model.mp417.48MB
12. 3.Evaluating, Improving, and Retraining Your Model/12.Splitting the Data into Training and Test Sets.mp47.5MB
13. 3.Evaluating, Improving, and Retraining Your Model/13.Retraining the Model with More Data.mp418.54MB
14. 3.Evaluating, Improving, and Retraining Your Model/14.Evaluating with Cross-Validation.mp415.98MB
15. 4.Classifying News into Subjects/15.Multiclass Classification and the UCI News Dataset.mp411.68MB
16. 4.Classifying News into Subjects/16.Using AutoML to Find a Suitable Model.mp412.25MB
17. 4.Classifying News into Subjects/17.Building the Pipeline and Evaluating the Performance.mp412.97MB
18. 4.Classifying News into Subjects/18.Explore the Effect of Imbalanced Data on the Metrics.mp415.41MB
19. 5.Building a Recommender System/19.The Restaurant Recommender.mp47.9MB
20. 5.Building a Recommender System/20.Building the Restaurant Recommendation Model.mp411.22MB
21. 5.Building a Recommender System/21.Exploring Hyper Parameters to Improve the Accuracy.mp441.99MB
22. 6.Classifying Images Using TensorFlow 'Transfer Learning'/22.Image Classification and Our Dataset.mp45.85MB
23. 6.Classifying Images Using TensorFlow 'Transfer Learning'/23.Deep Learning and Transferring Learnings from TensorFlow.mp415.32MB
24. 6.Classifying Images Using TensorFlow 'Transfer Learning'/24.Training the Custom Image Classification Model.mp421.35MB
25. 6.Classifying Images Using TensorFlow 'Transfer Learning'/25.Using the Trained Model in the Desktop Application.mp49.51MB
26. 6.Classifying Images Using TensorFlow 'Transfer Learning'/26.Speeding Up Model Training Using the GPU.mp423.97MB
27. 7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/27.What ONNX Is.mp46.2MB
28. 7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/28.The FER+ ONNX Model.mp416.83MB
29. 7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/29.Creating Our ONNX Pipeline.mp412.92MB
30. 7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/30.Detecting Emotions in Images and Webcam.mp421.56MB
31. 7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/31.Saving a ML.NET Model in ONNX Format.mp49.7MB