MAchine learning

Is Machine Learning Changing Our Lives?

Jun 11, 2021 | Technology Stack

If you are tech-savvy then you must have heard about Artificial intelligence (AI) and Machine learning (ML) applications. Machine learning applications are everywhere from being integrated into social media platforms to self-driving cars. The point is that machine learning is significant technology and is growing at a fast pace.

ML is a part of Artificial Intelligence, today we are going to give some examples of machine learning that we use every day.

1. Social Media Features

You must have noticed Facebook suggesting you specific accounts to follow or friends’ suggestions — all this is a part of machine learning. Social media platforms use machine learning algorithms to create suggestions that are appealing and have amazing features. Again if we talk about Facebook, it notices your chats, posts you have liked, and the amount of time you spend on those posts. Machine learning learns from a person’s choices and offers them the same of their interest.

Face Recognition: You post a picture of your friend and you, Facebook instantly recognize that friend. How does Facebook do this? It’s quite interesting — Facebook checks the pose and unique features in the picture and then matches them with the people in your friend list.

People You May Know: Another great feature but again how Facebook finds people that are related to you in some way? You have to learn one thing about machine learning that is- they work on a simple concept, understanding with experience.

Facebook keenly keeps an eye on the friends you connect with, the profiles you browse, your workplace, interests. On the mere basis of this understanding and experience, a list of ‘people you may know’ is created that you can possibly become friends with.

2. Product Recommendations

Wonder how you get product recommendations that suit your liking and interests often? Product recommendations are one of the best features of machine learning. Sometimes it even shows you mobile app trends from where you can buy the product. Using machine learning and AI technology a website track your choices and behavior based on your previous purchases and gives you perfect product recommendations.

3. Sentiment Analysis

To say it in simple terms, sentiment analysis is a real-time machine learning application that evaluates the opinion and emotions of the writer or a speaker. For example, if someone has written an email or a review, a sentiment analyzer will instantly figure out the tone and thought of the text. The best example of a sentiment analyzer is the Grammarly app.

4. Virtual Personal Assistant

Siri, Alexa, and Google Now are some of the most popular examples of virtual personal assistants. As the name speaks for itself, a virtual personal assistant listens to your command (your voice) and finds you information or calls someone and many other functions.

Of course, it is different from having a real human working for you, but still, a virtual personal assistant is the future of humankind. All you have to do is activate the assistant and voila you have a friend and assistant- two in one.

Machine learning is an important part of a virtual assistant as they are responsible for collecting and processing the information. Following are some examples of virtual assistants applied to a variety of platforms;

Smart Speakers: Google Home and Amazon Echo
Mobile Apps: Google Allo
Smartphones: Samsung Bixby on Samsung S8

We hope you now understand how machine learning has become a part of our daily lives. Please share some examples with us of how machine learning is a part of your life in the comments below. And if you want to work on something as exciting as AI then you can fill out the contact us form and we will get back to you!

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All this is great because you get a better and more personalized news feed. Here are some examples that you may recognize:

Face Recognition: You post a picture of your friend and you, Facebook instantly recognize that friend. How does Facebook do this? It’s quite interesting — Facebook checks the pose and unique features in the picture and then matches them with the people in your friend list.

People You May Know: Another great feature but again how Facebook finds people that are related to you in some way? You have to learn one thing about machine learning that is- they work on a simple concept, understanding with experience.

2. Product Recommendations

3. Sentiment Analysis

4. Virtual Personal Assistant

Smart Speakers: Google Home and Amazon Echo
Mobile Apps: Google Allo
Smartphones: Samsung Bixby on Samsung S8

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All this is great because you get a better and more personalized news feed. Here are some examples that you may recognize:

Face Recognition: You post a picture of your friend and you, Facebook instantly recognize that friend. How does Facebook do this? It’s quite interesting — Facebook checks the pose and unique features in the picture and then matches them with the people in your friend list.

People You May Know: Another great feature but again how Facebook finds people that are related to you in some way? You have to learn one thing about machine learning that is- they work on a simple concept, understanding with experience.

2. Product Recommendations

3. Sentiment Analysis

4. Virtual Personal Assistant

Smart Speakers: Google Home and Amazon Echo
Mobile Apps: Google Allo
Smartphones: Samsung Bixby on Samsung S8

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Difference between AI, ML, and Deep Learning

Jan 28, 2021 | Technology Stack

AI or Artificial Intelligence is the current buzzword on the streets. It has applications in almost all domains as well as markets. The private and Government sectors are equally excited about AI. So what is it?

In simple words, it is a technique, by which a machine or computer can exhibit decision-making intelligence on the basis of data, logic, and reasoning. Mostly, AI is governed by various algorithms for different applications, which are further powered by the computation of huge data. For instance, YOLO (You Only Look Once) is the algorithm by which a machine can detect various objects present in an image within a fraction of a second. Algorithms like this are which have made self-driven cars like Tesla a reality. In AI the algorithms are mostly classified into three categories:

Classification Algorithms: This type of algorithm is used for administered learning or supervised learning. It divides the subjected variable into different classes to be able to predict the class for a given input. One of the applications is to detect if the email is spam or not
Regression Algorithms: This type of algorithm can predict the output values based on input data points fed into the learning system. The common application of this is in the stock market or weather prediction.
Clustering Algorithms: This type of algorithm is used on unsupervised learning. The prime objective of this algorithm is to classify similar objects into groups.

Machine learning or ML is a subset or a branch of Artificial Intelligence. ML is a technique to train huge amounts of data to the system and based on algorithms, get predictive analysis. For instance, we can train the system with various attributes of fruits or flowers. Then we can enter some attributes as input and the system would predict which fruit or flower it is most likely to be. Unlike other computer programs, ML never gives boolean results. The only thing ML can prove is the probability or the likeness of some event to exist.

The Accuracy of a machine learning application is directly dependent on the amount and quality of the training data. In ML the algorithms are mostly classified into three categories based on their pattern of learning:

Supervised Learning: A model is created by training it with a sample dataset so that it can make predictions. The training process continues until the model achieves the desired level of accuracy on the basis of the training data set.
Unsupervised Learning: A model is prepared by deducing structures present in the input data. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Semi-Supervised Learning: In this process, there is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.

Deep Learning is a subset of machine learning only, which is dedicated to one specific problem or domain. In other words, if you use ML with a neural network to solve some specific problem, you have deep learning. A neural network is a series of algorithms, which are capable of detecting certain relationships in a set of data. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.

Few applications of Deep Learning can be found in deep fake, cancer detection, Natural Language Processing, and Colorization of black & white or grayscale images.