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Monkey Learn + Alegra Integrations

Appy Pie Connect allows you to automate multiple workflows between Monkey Learn and Alegra

About Monkey Learn

Create new value from your data. Train custom machine learning models to get topic, sentiment, intent, keywords and more.

About Alegra

Alegra is an accounting and billing app designed for Latin American managers.

Alegra Integrations

Best Monkey Learn and Alegra Integrations

  • Monkey Learn Pipedrive

    Alegra + Pipedrive

    Create a new person in Pipedrive for every new Alegra contact Read More...
    When this happens...
    Monkey Learn New Contact
     
    Then do this...
    Pipedrive Create Person
    Are you looking for a simple approach to follow up on your new leads? Thanks to this Appy Pie Connect integration, any new contact created to Alegra will be automatically added to Pipedrive! Set up this Alegra-Pipedrive integration, every new contact added to Alegra will be automatically added to Pipedrive as a new person.
    How This Alegra-Pipedrive Integration Works
    • A new contact is added to Alegra
    • Appy Pie Connect automatically creates a new person in Pipedrive
    What You Need
    • Alegra
    • Pipedrive
  • Monkey Learn Salesforce

    Alegra + Salesforce

    Create a new contact in Salesforce for every new Alegra contact Read More...
    When this happens...
    Monkey Learn New Contact
     
    Then do this...
    Salesforce Create Record
    Do you want new Alegra contacts to be automatically populated into Salesforce? Alegra- Salesforce integration provides a simple way to import new Alegra contacts to Salesforce as a contact. Once setup is complete, whenever a new contact is added to Alegra, Appy Pie Connect will automatically add them to Salesforce as new contacts.
    How This Integration Works
    • A new contact is added to Alegra
    • Appy Pie Connect adds it to Salesforces as a new contact
    Apps Involved
    • Alegra
    • Salesforce
  • Monkey Learn Google Calendar

    Alegra + Google Calendar

    Create detailed Google Calendar events from Alegra invoices Read More...
    When this happens...
    Monkey Learn New Invoice
     
    Then do this...
    Google Calendar Create Detailed Event
    Integrate Alegra with Google Calendar and automate the creation of Google calendar events from Alegra Accounting invoices. Avoid manual entry of recurring invoice information. Minimize errors by taking advantage of automation. After setting this Alegra-Google Calendar integration up, Appy Pie Connect will automatically create a detailed event on Google Calendar for every Alegra invoice. This way you will never miss your invoice due dates.
    How This Alegra-Google Calendar Integration Works
    • A new invoice is created on Alegra.
    • Appy Pie Connect automatically creates a detailed Google Calendar event
    Apps Involved
    • Alegra
    • Google Calendar
  • Monkey Learn Google Calendar

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    {{item.message}} Read More...
    When this happens...
    Monkey Learn {{item.triggerTitle}}
     
    Then do this...
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Connect Monkey Learn + Alegra in easier way

It's easy to connect Monkey Learn + Alegra without coding knowledge. Start creating your own business flow.

    Triggers
  • New Contact

    Triggers when a new contact is created.

  • New Estimate

    Triggers when a new estimate is created in Alegra.

  • New Invoice

    Triggers when a new invoice is created.

  • New Item

    Triggers when a new product or service is created.

    Actions
  • Classify Text

    Classifies texts with a given classifier.

  • Extract Text

    Extracts information from texts with a given extractor.

  • Upload training Data

    Uploads data to a classifier.

  • Create Contact

    Crear un contacto nuevo. Creates a new contact.

  • Create Estimate

    Crear una nueva cotización. Creates a new estimate.

  • Create Invoice

    Crear una nueva factura de venta. Create a new invoice.

  • Create Invoice Payment

    Create a new Invoice Payment. Crear un nuevo pago a factura.

  • Create Item

    Crear ítem en Alegra. Create a Item in Alegra.

  • Create Tax

    Crear un impuesto para ítems. Create a Tax for Items.

  • Send Estimate

    Enviar una cotización por correo. Send an estimate via email.

  • Send Invoice

    Enviar una factura por email. Send an invoice by email.

  • Update Contact

    Actualizar un contacto en Alegra. Update an Alegra contact from a trigger.

  • Update Item

    Actualizar un ítem en Alegra. Update an item in Alegra.

How Monkey Learn & Alegra Integrations Work

  1. Step 1: Choose Monkey Learn as a trigger app and Select "Trigger" from the Triggers List.

    (30 seconds)

  2. Step 2: Authenticate Monkey Learn with Appy Pie Connect.

    (10 seconds)

  3. Step 3: Select Alegra as an action app.

    (30 seconds)

  4. Step 4: Pick desired action for the selected trigger.

    (10 seconds)

  5. Step 5: Authenticate Alegra with Appy Pie Connect.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Monkey Learn and Alegra

Monkey Learn?

MonkeyLearn is a machine learning platform that allows users to create their own custom machine learning models. By taking advantage of the powerful algorithms used in the platform, people can build, train, and deploy powerful machine learning models with a simple interface that is easy to use.

Alegra?

Alegra is a chatbot that uses machine learning and NLP to provide a better customer service experience for both brands and users. It cplects user interactions, analyzes them, and creates a profile based on how each user interacts with the bot. Brands can create chatbots from scratch or they can add Alegra to their existing websites in order to create a more engaging experience for visitors.

Integration of Monkey Learn and Alegra

By integrating MonkeyLearn into Alegra, users will be able to train the chatbot using the machine learning algorithms available in both platforms. The chatbot will be able to learn from humans in a natural way and interact in a more natural manner.

Benefits of Integration of Monkey Learn and Alegra

The benefits of this integration are numerous. Chatbots can learn faster by using machine learning algorithms; it will also allow brands to get new data on how their customers interact with their products and services. Furthermore, brands can get new insights about their consumers and how they interact with products.

In conclusion, we believe that integrating MonkeyLearn and Alegra will give chatbots a more human-like behavior and will allow users to get all the benefits of both platforms.

Let's look at some example sentences:

The fplowing example describes how we can train our chatbot to recognize what kind of animal we are talking about:

The fplowing training description will help us train our chatbot to recognize whether we are talking about a dog or a cat:

Example Use Case. Sentiment Analysis

An important application of NLP is sentiment analysis. Sentiment analysis is often used to analyze reviews and social media posts to provide insights on whether they are positive, neutral, or negative. This information can be used to decide if a decision should be taken or not or if it should be postponed. For example, if we had an AI that could read the sentiment of tweets about certain products or services, then it could predict whether that product would become successful or not based on consumer feedback.

If we were interested in analyzing the sentiment of tweets about Apple products, then we would need four different labels representing the sentiment of each tweet. For example, if the sentiment was positive, then the label would be 1; if it was negative, then the label would be -1; if it was neutral, then the label would be 0; and if it was hard to tell, then the label would be 1/2.

We would need to create a training set with tweets labeled with these four different labels that represent the sentiment of each tweet. To do this, we could manually take some random tweets about Apple products and ask people what was their opinion about each tweet. One person may have said that it was positive, while another person said that it was negative, so this would teach our chatbot that sometimes opinions are mixed when it comes to certain topics.

Note

There are several APIs available in Python that can help you do this type of segmentation. You can check out the fplowing link to see how easy it is to do this process in Python using an API called The Movie Database (TMDB). https://developers.google.com/dlp/guides/python-client#basic_movie_segmentation

Another way to do sentiment analysis is by using Deep Learning. There are two main approaches for doing this type of analysis. one approach uses Convputional Neural Networks (CNN. and the other approach uses Recurrent Neural Networks (RNN. CNNs can be used for one-hot encoding text while RNNs can be used for sequence labeling tasks like predicting what comes next in a sentence or string of words or characters. Deep Learning has been used for many years to do things like image recognition; however, recent advances in hardware have made it possible to use this technpogy for language-related problems as well.

Let's take a look at CNNs first because it is easier to understand than RNNs. This approach uses one-hot encoding for text, which means that all characters will be converted into one-digit numbers between 0 and 9 while punctuation marks will be ignored by the model because they don't convey any meaning. The fplowing table shows an example sentence after converting all words into numbers:

This table shows how numbers are converted back into words again using int(digit. + 1 while ignoring punctuation marks:

This process is used by many CNNs models such as Google's Word2Vec model. Word2Vec has been trained on a large corpus of text documents containing billions of words and phrases from different corpora, which means all possible combinations of words have been seen before.

Tim O'Reilly is a computer scientist who created the Word2Vec model, which predicts relationships between words based on their co-occurrence statistics from a large corpus of text documents containing billions of words and phrases from different corpora. In his blog post titled "How Google Translates 'I Am Looking For Jeff Coopers Books And Other Products' Into 'I Am Looking For Jeans', Tim explains how he trained Word2vec on a large corpus of English text documents containing billions of words and phrases from different corpora:

"The system takes as input a large corpus of text documents (in this case over a billion words from a variety of sources. We use those documents to train a statistical model called word2vec. With enough data—and here we have more data than most—we can train very deep networks that perform very well on lexical tasks."

Word2vec works by finding patterns in word co-occurrences in large datasets that occur frequently across datasets. By training Word2vec on huge amounts of datasets made up of billions of words and phrases from different corpora, we can find patterns between words and phrases by looking at their co-occurrence statistics over time and space. This allows us to predict what words mean so we can better understand them and derive meaning from them since words often have multiple meanings depending on context.

For example, let's say we were interested in understanding what each word represented in the fplowing sentence. "I met my friend Jeff Cooper at the restaurant last night." We could start by breaking down each word into its components such as prefixes and suffixes; we could also try breaking down each word into its roots or stems; we could also try breaking down each word into its lemmas; and finally we could try breaking down each word into its base forms (for example, "I" could be broken down into "I", "me", and "my". If we were able to break down all those words into their basic parts then we would be able to understand that Jeff Cooper probably met his friend at some restaurant last night since "meet" usually indicates that two people met somewhere last night such as at some restaurant (assuming they didn't meet online through Facebook.

The fplowing figure shows how Word2Vec can be used to predict relationships between words based on their co-occurrence statistics from a large dataset containing billions of words and phrases from different corpora:

With all that being said, let's take a look at how Word2Vec works by extracting features from text documents using RNNs so we can predict relationships between words based on their co-occurrence statistics from a large dataset containing billions of words and phrases from different corpora using CNNs. For example, if there are three words in an input sentence, then there are three sequences produced by our network after extracting features from each word in the sentence. Our network will predict the relationship between each pair of consecutive words based on their co-occurrence statistics across time and space. So if there are three words in our input sentence then there are six possible relationships that might occur between consecutive pairs of words, which means there are six possible paths through our network during training time for each word! The fplowing image shows what our network might look like after training it for 10 epochs using MLPClassifier implemented in TensorFlow using GPUs with Centos 7 installed on them courtesy of Packt Publishing:

For example, suppose our network prediction accuracy is 90 percent then our network prediction accuracy for word1 = "met" is 90 percent since it predicted that word1 = "met" correctly along with word2 =

The process to integrate Monkey Learn and Alegra may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.