Introduction of Artificial Intelligence, Machine Learning and Deep Learning

Google IO 2017 has introduced the concept the AI, ML, and DL. Many developers are still not aware this concept. So in this tutorial, we will learn the basic concept of Artificial Intelligence, Machine Learning, and Deep Learning. Evidently, there is the high level of confusion around these terms. Let’s see meaning of individually.

Artificial Intelligence: Any technique which enables computers to behave like humans. It is the process of providing machine the power to think and decide what and how to do things they are meant to. I mean  If you talk to Siri on your phone and get an answer, you’re already close.

Machine Learning: Machine Learning is the subset of Artificial Intelligence that deals with the extraction of patterns from data sets. This means that the machine can find rules for optimal behavior but also can adapt to changes in the world. In machine learning you analysis the large set of data to find the patterns. Through the machine learning algorithm, we need to train to a computer such a way that it can understand the object model as human recognized.

Machine learning helps the mobile application to make decisions based users activities and through pattern recognition, it can take appropriate operations that are required without an even user ever asking it. Generally, it is very vast area to learn.

Deep Learning: It is a specific class of Machine Learning algorithms which use complex neural networks.  In a sense, it is a group of related techniques comparable to a group of “decision trees” or “support vector machines”. Deep Learning involves feeding a computer system a lot of data, which it can use to make decisions about other data.

Let’s group all three sets in below image to better understand.

Today as a data scientist, we are learning feature engineering as a part of machine learning skill. In that, we need to transform your data into the computer in a form that it can understand. For that, you may use Python or spreadsheet software to translate your data. Then you feed this data into the machine learning algorithm and it tries to learn from this data.

There are many ways to apply machine learning in an Android app. Which one is more suitable for you depends on tasks you want to solve with help of machine learning. For example:

  1. Machine learning algorithms can analyze your user’s behavior patterns and searching requests to make recommendations for him. It’s widely used in e-commerce apps, for instance.
  2. А video and audio recognition is also kind of machine learning. For example, you can use a face or fingerprint recognition to simplify authentication. Google is providing API for same to recognized object.
  3. You can also optimize the searching process by applying machine learning to your Android app. Add a voice search, spelling corrections, suggestions and the searching process for your users will become more intuitive and less troublesome. You can check some of the apps that made recently Correctify.

You can build intelligent apps using Tensorflow. Google recently open sourced TensorFlow, its software toolkit for building large-scale machine learning applications. It was a pretty big deal that Google gave away such important, capable technology for free.

Here’s an Android demo built with inception model for image identification. This app can identify simple basic objects such as notebook, mouse, water bottle, phone, books, pen, table chair etc look at the top texts of the photo it describes the object.

There are other open source models for natural language processing, computer vision, image captioning, generating words, text recognition or else you can train and generate your own models to use with Tensorflow for your own very need. is a natural language processing platform that makes it possible for developers to add intelligent conversation functionality to applications. Developers can use the API to add an intelligent voice or chat interface to home automation, connected car, smart TV, robotic, smartphone, wearable, and many other types of applications or you can build intelligent bot and got your application connected to the bot via API. Users can enjoy a hands-free mobile experience while driving, working out, cooking.

Wrapping Up: You have seen that machine learning is a hard to build in the application because as a human, our brain is wired to do all of this automatically and instantly. In fact, humans are too good at recognizing faces. Computers are not capable of this kind of high-level generalization, so we have to teach them how to do each step in this process separately.

For that, we need to understand many things behind this like statistics and probability, algebra, basic of python and API. Many developers are loving because it seems like machine learning and deep learning is future of upcoming information technology.

If you wondering Kotlin for android then I would be recommended to check all this post of Kotlin Category.

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I used reference for this article are Here and Here.

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Happy Coding 🙂

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