- [Instructor] The process for training a model starts … with finding source data that can be used to train it. … In many cases, you will need to pre-process … and clean up the data. … This may include pulling out irrelevant data, … incorrect data or combining different elements … to create information that the data … doesn't directly contain. … Once the data is pre-processed, … the data is separated into two sets. … The larger set for doing the initial training … and the second set to score the model … and see how accurate it is. … I tend to try and randomly separate … my source data into the two groups. … When the data is collected and separated into two groups, … we start with feature extraction, that is, … we manually pull information out of the data … in such a way that it creates defining characteristics … of what problem we want our model to solve. … For example, if we are trying to understand what someone … is saying, you may separate collection to phrases … into groupings based on what they are trying to do. …
- Defining machine learning
- Training a machine learning model
- Comparing machine learning frameworks
- Using IBM Watson for mobile machine learning
- Using Azure Machine Learning for speech and image recognition
- Training Core ML models
- Comparing client-side and server-side models
Skill Level Beginner
Xamarin and Android Studio: Material Designwith Kevin Ford1h 47m Intermediate
Machine Learning for iOS Developerswith Brian Advent1h 25m Advanced
1. Introduction to Machine Learning
2. Server Models: IBM Watson
3. Server Models: Azure Machine Learning
4. Client Models: Core ML
5. Understanding the Offerings
Next steps1m 40s
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