100+ AI Tools For Non-Coders That Will Make Your Marketing Better.

100+ AI Tools For Non-Coders That Will Make Your Marketing Better.

100+ AI Tools For Non-Coders That Will Make Your Marketing Better.

This is Pure Gold. AI Tools PDF.

Hi guys I am Arpit your AI For Marketing trainer. In today’s video we will talk about AI tools that can enable non coders to create and deploy sophisticated machine learning models.

I am hosting a free webinar about AI For Marketing. It will be like a crash course. You can register for the same. The link is in the description.

I strongly believe this video is pure value. I have worked really hard for this one. You can download all the resources PDF’s, links and a detailed explanation about the AI tools that I will just show you from the link given in the description.

I have covered more than 100+ tools divided in four major categories. If have go ahead and explain all of them in this video it will take me more than 2 hour. So I wanted to keep this video short. I will give you a short gist about these tools but if you are interested in full explanation you can download the resources from the link given in the description.

Ok Let’s begin the video.

Gone are the days when companies heavily relied on data scientists and data engineers. New age AI tools are quiet simple to use. They empower marketers to successfully execute complex machine learning projects without any code.

Global AI tools market is witnessing massive growth.

Here is a chart from statista https://www.statista.com/statistics/607716/worldwide-artificial-intelligence-market-revenues
According to the market research firm Tractica, the global artificial intelligence software market is expected to experience massive growth in the coming years, with revenues increasing from around 9.5 billion U.S. dollars in 2018 to an expected 118.6 billion by 2025.

The overall AI market includes a wide array of tools covering different aspects of AI such as web scraping, data mining, machine learning, data visualization, natural language processing, computer vision, conversational UI, robotic process automation.

Top 4 companies that offer the entire suit of AI services in which a lot could be used by non-coders our Google, Microsoft, Amazon and IBM.

These companies offer more or less everything currently possible within the scope of AI.

All 4 companies are leading the charge for providing Machine Learning as a service (MLaaS). Each platforms has its own pros and cons.

Which platform is best for you depends on what you want to accomplish.

All these four companies offer separate set of tools for performing automated ML and AI tasks and a separated platform for custom modelling. Service is offered at two levels:-

  • For beginners all of them have their automated platforms through which you could achieve a lot of ML and AI based tasks without any code.
  • For experienced data scientists that want to build more complex custom models. All of them support major frameworks and have built in algorithms for quick modelling.

For easier understanding we can break down the comparison into four major categories – Predictive analytics tasks, natural language processing API’s, image analysis API’s and video analysis API’s.
If you not aware about these categories I reckon you first watch my video about application of AI in marketing where I explain them in detail.

Let’s start by comparing top machine learning as a service vendors for predictive analytics tasks.

So like I said Amazon, IBM, Microsoft and Google are leading the charge with AI tools. We have linked to each of these platforms when you hover on their names you can see their respective links.

In the first column we have mentioned all the predictive tasks like classification, regression, clustering, Anomaly detection, Association rule learning and ranking. I’ll explain these in a bit. First know this A tick mark indicates the service is offered by that company and a cross indicates the service is not offered. Then you can also see we are using different colours for ticks marks. An orange tick mark reflects no code is required, these our fully automated tasks which could easily be performed by non-coders. A green tick mark, which you will see in the coming comparisons, reflects little code is required. What we mean by little code is that in all most all the cases the code is already given, you just need to copy paste it in the notebook that you are using for executing that task. A little basic knowledge of python is required for executing these tasks. With little effort it could be performed by non-coders. Then finally we have a pink tick mark which reflects custom coding is required to execute these tasks. You will need a developer as you need to write custom code for performing these tasks.

Ok, so what are these predictive tasks what can you do from them. Let’s understand them in a simple language.

There are several subclasses of ML problem based on what the prediction task looks like. In the table below, you can see examples of common supervised and unsupervised ML problems.

Now that you understand what are predictive tasks let’s discuss about each company and what they have to offer.

Predictive Analytics with Amazon ML

Amazon Machine Learning is one of the most automated solutions on the market. All data preprocessing operations are performed automatically: The service identifies which fields are categorical and which are numerical, and it doesn’t ask a user to choose the methods of further data preprocessing.

Prediction capacities of Amazon ML are limited to three options: binary classification, multiclass classification, and regression. They don’t support any unsupervised learning methods. A user isn’t required to know any machine learning methods because Amazon chooses them automatically after looking at the provided data.

IBM Watson Machine Learning Studio

IBM studio has an AutoAI which brings a fully automated data processing and model building interface that needs little to no training to start processing data, preparing models, and deploying them into production.
The automated part can solve three main types of tasks: binary classification, multiclass classification, and regression. You can choose either a fully automated approach or manually pick the ML method to be used. Currently, IBM has ten methods to cover these three groups of tasks. I wont cover them here but you can download the resources to know about them.

Microsoft Azure Machine Learning Studio

The roster of Microsoft machine learning products is similar to the ones from Amazon, but Azure, as of today, seems more flexible in terms of out-of-the-box algorithms.
Another great thing is Almost all operations in Azure ML Studio must be completed using a graphical drag-and-drop interface. This includes data exploration, preprocessing, choosing methods, and validating modeling results.
Perhaps the main benefit of using Azure is the variety of algorithms available to play with. The Studio supports around 100 methods that address classification (binary+multiclass), anomaly detection, regression, recommendation, and text analysis. It’s worth mentioning that the platform has one clustering algorithm (K-means).

Google Cloud AutoML

Google Cloud AutoML is a cloud-based ML platform tailored for inexperienced users. Customers can upload their datasets, train custom models, and deploy them in the website. Needless to say, AutoML is fully integrated with all Google’s services.

There are several products available with AutoML that you can access via a graphical interface. To briefly describe them, these are image and video processing services, a natural language processing and translation engine, and training models on structured data. Since each product can be accessed via an API, we’ll cover them separately.

To wrap up with machine learning as a service (MLaaS) platforms for predictive analytics tasks, it seems that Azure has currently the most versatile toolset on the MLaaS market. It covers the majority of ML-related tasks, provides two distinct products for building custom models, and has a solid set of APIs for those who don’t want to attack data science with their bare hands.

Guys, I just realised that this video is becoming too long. I want to keep it around 10 minutes. I know I haven’t covered the other three categories. Let me see how do you guys react to this video if you like it let me know in the comment box. I can make a part 2 and continue this video only if I get a good response. And if you really want to learn then like I said the resources are in the description. Go ahead and download them.

Also you can attend a free webinar that I am hosting to learn more about AI for marketing.
Honestly, I haven’t even covered predictive tasks in depth. Let me know if you find this valuable I can make a part 2 video. All depends on how you guys respond to this one.

I always try to bring new stuff that nobody is talking about so consider subscribing and hit the notification bell if you don’t want to miss out. If you learnt something new toady make sure you smash that like button.
Thank you so much for watching. See you in the next one.

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