Introduction
Today, Data Science Applications beat nearly every industry on the planet. There is no single industry in the world now that has not relied upon data. In conclusion, data science has become a source of energy for businesses. Data Science Applications have yet to take on the function within a night. We can now anticipate outcomes in minutes, which was earlier used to take many human hours for processing due to faster computers and cheaper storage.
Data scientists are addressing Future-oriented questions. They start with big data, which has three features: volume, variety, and velocity. The information after that is used to feed algorithms and models. Models that autonomously self-improving, recognizing and learning from their failures, are made by the most cutting-edge data scientists working in machine learning and AI.
You may also like to read about – How To Become A Data Scientist In 2023
Data Science Applications & AI applications used in daily life
Email Spam Filtering
The large volume of rejected spam emails has made an intense requirement for developing more dependable and robust antispam filters. Current machine learning methods are successfully detecting and filtering spam emails. Let us understand this concept with a simple example.
Assume that we have a dataset of 30,000 emails, out of which some are classified as spam, and some are classified as not spam. Will train the machine learning model on the dataset. Once the training process is complete, we can test it with a mail not included in our training dataset. The machine learning model and Data Science Applications can make predictions on the following input and classify it correctly if the input email is spam or not.
The principal methodology behind detecting if the provided email is — spam or not is by detecting patterns of fake emails and words that are usually used while promoting or advertising products for customers with over-the-top discounts or other similar ways.
Various machine learning algorithms like Naive Bayes, support vector machines, K-nearest neighbors, and random forests, among many other algorithms, can be used for filtering spam messages and classifying if the received email is a “spam message” or not. Advanced spam detection can be performed using techniques like neural networks or optical character recognition (O.C.R.), which companies like Gmail for spam filtering also use.
Autocomplete
Autocomplete, or word completion, is a feature in which an application predicts the rest of a word a user is typing. In Android smartphones, this is called predictive text. In graphical user interfaces, users can typically press the tab key to accept a suggestion or the down arrow key to accept one of several.
As we type in “what is the wea..” we already receive some predictions. These predictive searches also work on A.I. These usually work on concepts such as natural language processing, machine learning algorithm, and deep learning. A sequence-to-sequence mechanism can achieve higher accuracy and lower losses for these predictions.
Zero-shot and one-shot learning methods even exist for natural language processing. Can use the same methods for better training of the model to improve the overall performance and avoid repeated training procedures which can be a significant hindrance in some real-life applications and scenarios. Hence, one-shot learning is an excellent alternative for deployment and working in other embedded systems with lower training capacities.
The next word prediction for a particular user’s texting or typing can be excellent. Understanding the user’s texting patterns would save a lot of time. This could also be used by our virtual assistant to complete specific sentences. Overall, the predictive search system and next-word prediction are fun concepts to implement. You can check out my article below, which covers the deep learning methodology to predict the following words.
Auto-correction
Auto-correction, also known as text replacement, replace-as-you-type, or autocorrect, is an automatic data validation function commonly found in word processors and text editing interfaces for smartphones and tablet computers.
Auto correct based on A.I. methodologies is highly beneficial to achieve the best results while texting or typing to avoid incorrect statements or words. The spellings are automatically checked and immediately corrected to the nearest good values. However, if the quality of your A.I. training could be better, it can cause specific errors, and you might send a message you did not want. Jokes aside, For the most part, autocorrect does a tremendous job of correcting misspelled words while texting quickly.
The process of auto correct involves four main steps:
- Identifying a misspelled word.
- Finding the strings while computing the minimum edit distance from each of them.
- Filtering the possible candidates for the right word selection.
And finally, calculate the word probabilities to forecast the best possible prediction for the particular word.
The method mentioned above is one of the ways to compute the autocorrect problem with the help of machine learning algorithms like logistic regression or Naive Bayes. However, c can also use deep learning methods to solve similar problems. If you are interested, let me know in the comments below, and I will make an article covering the following in further detail.
Face recognition
Face recognition is the procedural recognition of a human face and the user’s authorized name. Face detection is more straightforward and can be considered a beginner-level project. Face detection is one of the steps that is required for face recognition. Face detection is a method of distinguishing the face of a human from the other parts of the body and the background.
The haar cascade classifier can be used for face detection and accurately detect multiple faces in the frame. The haar cascade classifier for frontal face is usually an XML file that c can use with the open-cv module for reading the faces and then detecting the faces. A machine learning model such as the histogram of oriented gradients (H.O.G.) can be used with labeled data and support vector machines (SVMs) to perform this task.
The best face recognition approach is D.N.N.s (deep neural networks). After detecting faces, we can use the approach of deep learning to solve face recognition tasks. There is a wide variety of transfer learning models like VGG-16 architecture, RESNET-50 architecture, face net architecture, etc., which can simplify the procedure to construct a deep learning model and allow users to build high-quality face recognition systems.
You can also build a custom deep-learning model for solving the face recognition task. The modern models built for face recognition are highly accurate and provide an accuracy of almost over 99% for labeled datasets. c can use the applications for face recognition models in security systems, surveillance, attendance systems, and much more.
Below is an example of a face recognition model built by me using the methods of VGG-16 transfer learning for face recognition after face detection is performed by the haar cascade classifier.
Virtual Assistant
A virtual assistant, also called an A.I. assistant or Data Science Applications, is an application program that understands natural language voice commands and completes tasks for the user. Virtual Assistants powered with A.I. technologies are becoming extremely popular and are taking over the world by storm.
We have virtual assistants like Google AI, Siri, Alexa, Cortana, and many other similar virtual assistants. With the help of these assistants, we can pass commands, and using speech recognition, it tries to interpret what we are saying and automates/performs a realistic task. Using these virtual assistants, we can make calls, send messages or emails, or browse the web with a simple voice command. We can also converse with these virtual assistants, and hence they can also act as chatbots.
The power of Virtual Assistants powered by Artificial Intelligence is wider than smartphones or computer devices. You can also use them in IoT devices and embedded systems to perform tasks effectively and control your entire surroundings. An example is home automation using a Raspberry Pi, where you can control your entire house with a simple voice command.
The combination of A.I. and IoT is a big deal as they produce excellent results. The integration of artificial intelligence in embedded IoT devices like raspberry pi and, Nvidia, Jetson Nano (Among many others) can develop some masterpieces which will be highly profitable and beneficial to society as a whole. Some famous examples of virtual assistants like Alexa, Siri, or Google A.I. show the high-level intellect and future possible.
