Microsoft To Do is the task management app that makes it easy to stay organized and manage your life. It's simple, smart, and a whole new way to get work done in less time.
Create new value from your data. Train custom machine learning models to get topic, sentiment, intent, keywords and more.Monkey Learn Integrations
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Triggers when a new list is created.
Triggers when a new task is created.
Triggers when any task is update.
Creates a new list.
Creates a new task
Classifies texts with a given classifier.
Extracts information from texts with a given extractor.
Uploads data to a classifier.
Microsoft To-Do is a powerful task management application which offers, to the users, a smart and easy way to manage their daily tasks. By using Microsoft To-Do, you can create different lists of your tasks, edit, prioritize, see your progress on each task, etc. It helps you to be more organized and productive by keeping your focus on what matters most.
Monkey Learn is an online machine learning platform that allows anyone to build machine learning or deep learning models with just a few lines of code. It helps you to create innovative applications by using the machine learning models that are provided in the platform. The integration between Microsoft To-Do and Monkey Learn gives users an easy way to add intelligent features to their To-Dos.
To add intelligence into your Microsoft To-Do list, you will need to use Monkey Learn. This is a simple but powerful top for machine learning and natural language processing. It allows you to build different models for text classification, translation, sentiment analysis, and image labeling. You can use these models to help you in your daily life and work in an efficient and accurate way.
You can use Monkey Learn to create a model that will allow you to classify the tasks in your Microsoft To-Do list by priority. So when you will add a new task in your To-Do list, the app will automatically suggest the priority level for this task based on how you have classified the other tasks. You don’t need to take time to manually classify each task. The result of this approach will be faster and more accurate than human-based classification.
Another option is to create a model that will give you suggestions for the next step in order to complete a specific task. You can copy and paste any message from Microsoft To-Do into Monkey Learn and then train it to give you suggestions. For example, if you have a task that should be completed by adding a contact from your address book, Monkey Learn will suggest you add to your To-Do list the name of the contact. If you have a task that should be completed by sending an email to a specific person, Monkey Learn will suggest you add the email address of this person. After training the model, you can copy and paste any message from Microsoft To-Do and receive a suggestion to complete your task.
Besides text classification, another common use case for machine learning is automatic categorization of items from images. In this case, Monkey Learn can suggest tags for your tasks based on the content of images that are attached to them. For example, if you have a task that has been added with a picture of a dog, Monkey Learn can suggest tags such as pet, animal, etc. In this case, you will not need to manually tag each item every time you add a new task. You can train a model once and let it do the tagging for all tasks in your To-Do list.
The main benefit of integration of Microsoft To-Do and Monkey Learn is that the users will save time on manual classification of items in their To-Do lists. Users no longer need to do this task manually because there is an intelligent feature that does it automatically based on how they have classified other tasks in their To-Do lists. They can train one model once and then it will work with all tasks in their lists automatically in the future without requiring any additional effort on their side. Users also save time on tagging in images in their To-Do lists because they don’t need anymore in most cases to do it manually with each item in their lists.
Another benefit is that users get better results with classification and tagging because these features use machine learning algorithms instead of human-based classification and tagging which means these features are more accurate than manual classification or tagging.
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