Why You Should Introduce AI To Your product?


Akshat Mathur

24 Dec 2018

Artificial Intelligence

While working in the field of Artificial Intelligence, the most important thing is to be a continuous learner, The amount of research going on in this field is tremendous and it is getting upgraded every day, even as you are reading this article there might be another breakthrough in AI.


So what are we trying to achieve with AI?


To answer that question we would have to look at the state-of-the-art work that has been done till now in AI and the answer would be as clear as it would be “To Provide Machine with the capabilities such as Humans”.


Speaking of Human-like capabilities, AI has Natural Language Processing (NLP), with NLP, we are providing machines with language capabilities.


Today,  we can enter our house and can control every surrounding accessory with just a call of our voice(Alexa, Siri, Google home) are some of the voice assistants that came into existence only because of NLP.

In NLP, we achieved a milestone when Google recently showcased their Duplex in which an AI agent makes calls on the user’s behalf and makes reservations/appointments for them.


I hope someday we might have our very own personal Jarvis #starkfan


By now, you must have gotten an idea on how cool AI is. Many of us want to pursue this field but are too afraid to do so because of the jargon it has like Backpropagation, GAN, neural nets. But to truly understand AI you need to recognize these jargons.


So, in this article, I will try to break some of these things down in the simplest manner possible, so that by the end of this article you will know why AI is important and why you should provide AI capabilities to your products, what are different subfields of AI and the fundamental concepts of Applied Machine Learning.


So Let’s Start with “WHY”. Why is Artificial Intelligence important and why should you think about integrating it with your product?






Subfields of Artificial Intelligence

To sum up AI in two components,


AI = Large Dataset + Intelligent Algorithms


It learns from the patterns or features in the data with the help of progressive algorithms. AI is a broad field of study that includes many theories, methods, and technologies, as well as the following major subfields:








Applied Machine Learning Process


Once you get into solving Machine Learning problems, you will start identifying the pattern that you can apply on all your ML projects.


In this section of the article, I have shared a process that can be used as the kick-off or template for your next Machine learning project


Always start with “WHY”


Before solving any ML problem you need to DEFINE the problem.


Defining an ML problem


Step 1 Why does it need to be solved?


For example, you might be trying to solve a problem as a learning experience or to improve the user experience of your existing products.


Step 2 What are the benefits of the solution?



Step 3 Define the steps to solve the problem



Gathering Data

Every Machine Learning Solution starts with Data Collection. This part involves the visualization of the data, More is always better, In Machine Learning, larger the dataset, better will be your accuracy.

Cleaning The Data

Not all datasets are as clean and pretty as you can find on Kaggle, in real life data is always messy and any Machine Learning model is as good as the data it is getting trained on. Always try to reach the maximum sophisticate in your data, Once the data is collected, the next step is to clean your data as much as possible and you can achieve that by following some rules




Luckily we live in the age of open source, there are many Python open-source libraries available the makes this mundane task much more bearable. Some of which are listed below.


These Libraries have different use cases. You can find one which solves your use-case.


Data Sampling

Earlier, I’d said that more is always better when it comes to Machine Learning, but here is a little twist, more data can also result in more computational and memory requirements. So it’s always better to have a smaller set of your data, a sample from your data that you can test on your algorithm. This will be much faster and computationally less expensive before you go for the complete dataset.

Data Representation

Your machine understands data in forms of numbers. Similarly, input for any Machine learning model will always be in numerical form. So you always need to make sure that your data representation is such that your algorithm can understand.


Now, finally, it’s time to get some actual results from those data by feeding it to your Algorithm. But wait, is that all? Nope.


Feeding those data to any machine learning algorithm will give you the results, but does it mean that it has the highest accuracy rate? No, definitely not.


Before choosing your algorithm you must try to feed your sampled data to as many machine learning algorithms as you can. That will help you get the highest accuracy rate amongst all.


This way, you can evaluate the most suitable Machine Learning algorithm for your dataset. We will have a separate blog on How to Measure the Efficiency of a Machine Learning Algorithm. So, stay tuned for that.

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