Top 10 concepts and Technologies in Machine learning

Top 10 concepts and Technologies in Machine learning

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Updated on Sep 7, 2022 16:51 IST

This blog will make you acquainted with new technologies in machine learning


Machine learning is the process of teaching computers to learn from data, without being explicitly programmed. It enables computers to make decisions and predictions by recognizing patterns in data. The applications for machine learning are endless – it can be used for anything from detecting credit card fraud, to automatically tagging friends in photos on Facebook, to predicting sales trends.

The field of machine learning is constantly evolving, with new concepts and technologies being developed all the time. In order to stay ahead of the curve, it’s important for data scientists to Stay up to date with the latest advancements by following some of these blogs. This will help you to understand how machine learning can be applied in practice and give you ideas for potential applications in your own business or field of work.

What is machine learning?

Machine learning is a field of computer science and artificial intelligence involving the development of algorithms that allow computers to learn from data, without being explicitly programmed. The concept of machine learning was first introduced by Arthur Samuel in 1959. He described it as a way for computers to learn from experience, and improve over time. The first practical application of machine learning was in speech recognition, where machines were taught to understand and interpret human speech. For knowing different applications of machine learning you can visit my previous blog on the Future of machine learning.

Different technologies: Machine learning

1. Deep Neural Networks (DNN)

Deep neural networks are a subset of machine learning algorithms that have been around since the 1950s. DNNs can perform tasks like image recognition, speech recognition, and natural language processing. They consist of multiple hidden layers of neurons where each layer learns a representation of its input data. These representations are then used to make predictions about the output data.

2. Generative Adversarial Networks

Generative adversarial networks (GANs) are a type of generative model that trains two competing neural networks against each other. One network tries to generate samples that look realistic, while the other evaluates whether those samples come from real data or generated data. GANs have shown great success in generating images and videos.GANs are used to generate new data that resembles the existing data but is in fact totally new we can use GANs to produce new images from existing masterpieces made by renowned artists also known as contemporary AI art they are artists working with the generative models produced masterpieces already you can find out a few of the artists who are using AI and ML for their contemporary art. We are using the existing data and we are generating new images. 

3. Deep Learning

Deep learning is a subset of machine learning that uses multiple processing layers (usually hundreds) to learn data representations. This allows computers to perform tasks that are difficult for humans. Deep learning has been used in many fields, including computer vision, speech recognition, natural language processing, robotics, and reinforcement learning.

4.COVID-19:Machine learning and Artificial intelligence


Artificial Intelligence (AI) has been used to detect COVID-19 cases in China since January 2020. This AI system was developed by researchers at Wuhan University. They have created a deep learning model that can analyze data from mobile phone calls, text messages, social media posts, and other sources. This AI system can predict the spread of the virus based on the number of people who are infected. Some companies used machine learning and AI to detect disease outbreaks and also they identified the high-risk patients as well so machine learning now is helping organizations to speed up the process of finding out the possible treatment or

5. Conversational AI or conversational BOTS

It is a technology where we speak to a chatbot and it processes the voice after recognizing the voice input or text input as well and then a certain task or a response is enabled like

Real-life examples

Google Assistant, Alexa, and Siri. You see a lot of chatbots on many websites on product landing pages where they will simply give you a response based on the input. But what’s new now? Now conversational bots in the form of virtual assistants are enabling a new ground for customer engagement at the next level where organizations will be going for cognitive conversational AI where a part is efficient enough to understand the context and dialogue as human behavior so this will shape the future of conversational AI which will enable more ground of studies related to human

6. Machine learning in cybersecurity 

Cybersecurity is a domain where it is made sure that an organization or anyone for that matter is safe from all security-related threats on the Internet or where there is a network involved. An organization deals with a lot of sophisticated data that needs to be saved from malicious threats like anyone trying to break into your server or trying to get access to your data or unauthorized access so that is the cyber security. With machine learning, it becomes quite easier to study the previous data to make alerts for the upcoming threats. So let’s say we have a company or we have cybersecurity having a lot of previous year’s data where so many malicious attacks or malicious threats happened. We can use that data to train a model which will eventually prevent our help in maintaining the system and make it more secure so more and more companies will look for machine learning-related solutions to tackle the security threats.

Confusion Matrix in Machine Learning
Confusion Matrix in Machine Learning
Are you tired of your AI models getting confused? Untangle their mysteries with the Confusion Matrix, your secret weapon for accuracy! Decode True Positives, False Negatives, and more to more
Overfitting and Underfitting with a real-life example
Overfitting and Underfitting with a real-life example
One hot encoding vs label encoding in Machine Learning
One hot encoding vs label encoding in Machine Learning
As in the previous blog, we come to know that the machine learning model can’t process categorical variables. So when we have categorical variables in our dataset then more

7. Machine learning and IoT

The various IOT processes that we use in the industries are subject to a lot of errors I mean after all it’s a machine. Maybe the machine is not programmed properly or there are a few vulnerabilities it is bound to fail at some point. But with machine learning maintenance becomes a lot easier because all the factors that may lead to a failure in the ID process will be identified beforehand and a new plan of action can be prepared for that matter so which in turn will help the companies to save an ample amount of money by cutting the maintenance cost 

Real-life example

We have smart homes or connected cars so there are a lot of vulnerabilities with the connected car. There are a lot of things that can go wrong in the car system so machine learning will identify the pattern and it will create alerts or you will know that your system is actually requiring some maintenance.

8. Augmented reality

Augmented reality is the future of AI. The applications that augmented reality(AR) has potential will include a lot of real-life applications 


Real-life examples

we see in everyday shopping experience has become automated 

Gaming is moving towards a more inclined approach towards augmented reality combined with virtual reality to provide the next level gaming experience to a user and virtual search is one more application that is the future with ML

9. Automated machine learning


Traditional machine learning model development required significant domain knowledge and time to produce and comparison of dozens of models. And was more time-consuming resource-intensive, and challenging. Automated machine learning, helps in getting production-ready ML models with great ease and efficiency. Required more expertise

But automated machine learning changes that, making it easy to build by running Automated processes on raw data and pulling the most relevant information from the data by selecting models.

We have different python automated libraries like

  • HyperOpt
  • AutoML
  • TPOT
  • Autoscrapper
  • Octoparse
  • PyAutoGUI
  • Pandas Profiling
  • H20
  • Autokeras
  • AutoGluon

10. Time-series forecasting


Doing forecasting is an integral part of any type of business, it could be sales, customer demand, revenue, or inventory. Combining with automated ML can get a recommended, high-quality time-series forecast. Now, what is time-series data? It is an observation from the successive time intervals. Machine learning can give improved results if new data is fed constantly. Advanced forecasting configuration includes:

  • Earthquake Prediction Model
  • Daily Births Forecasting
  • Stock Price Prediction


I hope you got the clarity of what type of development is going on and what can happen in the near future. In this, I have listed a maximum of the new trends and technologies with real-life examples. If you want to know more about the applications of machine learning you can read my previous blog. I also wrote on different topics of machine learning with python code. And try to explain it in an easy language.

I hope you enjoyed reading this blog!!! If yes then consider hitting the stars below and sharing this blog with your friends and creating more awareness about this topic.

Happy Learning!!!

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This is a collection of insightful articles from domain experts in the fields of Cloud Computing, DevOps, AWS, Data Science, Machine Learning, AI, and Natural Language Processing. The range of topics caters to upski... Read Full Bio