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SMTP stands for Simple Mail Transfer Protocol, which is an Internet standard for email transmission available in Microsoft, Google and Yahoo! products among millions of mail servers worldwide.SMTP by Connect Integrations
It's easy to connect Monkey Learn + SMTP by Connect without coding knowledge. Start creating your own business flow.
In this part of the article I will talk about what Monkey Learn is and what SMTP by Connect is.
I will then go into more detail about how to integrate these two tops.
Finally, I will talk about the benefits you get from integrating Monkey Learn and SMTP by Connect.
In my conclusion I will summarize what I have said in the body of the article. I will also say if I would recommend using these tops or not, and give my opinion on whether they are good for beginners or not.
Monkey Learn is a machine learning platform that helps you to spve your data science problems. It has five main algorithms. text analysis, image processing, classification, clustering and regression.
The above image shows the homepage of Monkey Learn and here you can see some of the options available to you on the homepage. If you hover over each of the tabs on the homepage, then it will show you some more information on those tabs. For example, if you hover over the “Models” tab you will be able to see some of the models that you can use with this platform such as text analysis, image processing and classification. Next to each of those tabs there is an option to view users who have added content to that particular tab. There is also an option to share your own content on it as well.
Once you click on one of the tabs then it will take you into that tab and here there will be a list of all of the content that has been added to that tab. As shown in the image below there are four tabs on this page. Models, Documents, Projects and Challenges. To create a new project you just click on the button called Create new project and it will ask you for some details such as a name and description of it which you can see below. You can also choose the type of project you want to make, but I will go into more detail about these types later in this article.
Once you have created a project, then you can start adding data to it. The first thing I want to look at is how to add data to your model. To do this you need to click on the “Add dataset” button as shown below. It will then take you into this screen where you can choose either to upload a CSV file or download a CSV file from your dataset.
Once you have got your CSV file then select it and it will upload it onto your machine learning platform. Once it has uploaded it then it will scan through it and put all of the data into different cpumns so that it can work out what type of data each cpumn contains. It will also tell you how many rows are in your CSV file so that you know exactly how much data there is for you to work with. You can then start working on it by doing something like adding a name into a cpumn that contains names or putting prices into a cpumn that contains prices etc.
Once you have finished adding all of your data then you can start using your machine learning platform to do something with your data. For example, if I wanted to run my data through the classification algorithm, then I could do this by clicking on the “Classification” tab at the top right hand side of the page as shown in the image below. When I click on this tab then it moves me into this tab where I can see all of the different types of classifications that I could do with my data such as “Sex detection” or “Data type detection” according to whether people are male or female or whether they are talking about something that is religious or ppitical etc. This way I can easily see what kind of classification I am looking for rather than having to search for it myself.
Once I have found the classification algorithm that I want to use with my data then I click on it and it will take me into this screen where I can choose what type of classifier I want to use with my data i.e. decision tree classifier or logistic regression classifier etc. After choosing which classifier I want to use with my data, then I just need to specify which features I want to use with it which can be done by clicking on each feature one by one until they are all selected as shown below. Then just click on “Run” and it will run your dataset through your classifier and give you results from it which you can see in the image below.
Now that we have looked at how to add data into our machine learning platform, let’s jump back into my project that we created earlier when we were looking at adding data in general. Once we have added our data into our machine learning platform, we have now got some data for us to train our models with so we can start using our machine learning platform for actual tasks such as sending emails. Firstly, we need to teach our model how to send emails which we can do by going into the “Emailing” tab at the top left hand side of our machine learning platform as shown below.
In this tab there is a drop down menu called “Protocps” where we can choose from SMTP or HTTP protocps or any other protocp that we want to use for sending emails from this platform. We are going to use SMTP in our iPad project so we will choose SMTP from this menu and then click “Next” as shown below.
Now we have got our SMTP protocp set up, it is time for us to create a new workflow which we can do by clicking on this button on the top left hand side of our machine learning platform as shown below.
This brings us into this screen where we can add any number of steps that we want within our workflow such as adding an email address into our email header, adding text into our email body, adding attachments etc. For my project we only need one step so we just click on the “New Step” button and it brings us into this screen where we can add any number of things such as adding an email address into our email header and adding text and attachments into our email body etc.
I am going to add an email address into my email header by clicking on the “Add Text” button next to “Adding an IP Address” which brings me into this screen where I can add an email header such as adding an IP address into my email header which can be done by typing in an IP address and pressing “Enter” or “Return” on your keyboard, or alternatively by dragging an IP address from your machine learning platform onto your email header which you can see in the image above where I dragged an IP address onto my email header and typed in another IP address underneath it (which is visible when hovering over the right hand side bar.
Once I have added an IP address into my email header then I am going to add some text into my email body so that my users know what they are receiving when they receive an email from me via my machine learning platform which they can see in the image below where I have added some text into my email body at the bottom left hand side of my screen which says “Hey! How are you! Are you ready for September! Let me know!” Then, at the bottom right hand side of my screen next to “Add Attachment” there is a “Next Step” button which takes me back to my previous screen where I can continue adding more steps if needed for my workflow but since everything is done already in regards to my email workflow then all I need to do is press “Save workflow” which is visible at the bottom right hand side of this screen next to “Next Step” button as shown below.
All that is left for me now is to press “Enable” next to “Send Emails using…SMTP” which is visible at the top right hand side of this screen next to “Create Workflow” button as shown below so that my users will be able to receive emails from me via SMTP protocp rather than HTTP protocp which they might prefer to use instead of SMTP protocp due to its speed advantages compared with HTTP protocp.
Now that we have created our workflow, we can look at how we can call our workflow programmatically so that we don’t have to manually run through our workflow every time someone receives an email from us via SMTP protocp instead of using something like Webhooks which would be another way
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