All About Machine Learning

What is machine learning? | Fortinet Machine learning is a subset of artificial intelligence. This technology can mimic the human brain, identify data security vulnerabilities, and predict bad loans. Listed below are some examples of applications of machine learning. First, we will explore some of the main types of algorithms and their uses. To start, classification algorithms address categorical output variables. These types of algorithms are helpful in spam detection and email filtering. Regression algorithms, on the other hand, deal with continuous output variables. Popular algorithms include the Multivariate Regression Algorithm, Decision Tree Algorithm, and Lasso Regression.

Subset of AI

Artificial intelligence (AI) includes machine learning and other intelligent systems. Both are based on learning from experience. Machine learning uses two types of algorithms: supervised and unsupervised. The computer is given training data in supervised learning, such as correct and wrong answers. The algorithm is then allowed to learn from that data. Unsupervised learning uses a different method, where the machine is left to make mistakes on its own.

Machine learning is a subset of artificial intelligence. This artificial intelligence uses algorithms to automatically learn insights and patterns from data and applies them to make better decisions. These programs can help people and businesses with various tasks. For example, deep learning models employ substantial neural networks like human brains to analyze data and make predictions without any human input. Eventually, these computer systems will be able to do more tasks than a human would.

Mimic a Human Brain

A neural network mimics the human brain’s structure and functions of neurons. In experiments, the neural network has been shown to recognize objects, create photorealistic pictures of fake celebrities, and even translate thought into text. A neural network is similar to the human brain, processes information, and learns. A neural network is also known as an artificial neural network. This technology is used in many fields, from artificial intelligence to medicine.

The new research could allow computers to learn from their surroundings. The team has developed a device based on synaptic transistors. These transistors process information in parallel and fault-tolerant ways. These transistors are made of organic plastics that mimic a biological synapse. It has the potential to save space and energy by bringing memory and logic together in a single chip. The new device could be integrated into implantable electronics, intelligent robotics, and wearable devices.

Identify Data Security Vulnerabilities

Machine learning technology can identify data security vulnerabilities by analyzing network traffic patterns. This is particularly helpful when organizations deal with zero-day attacks and vulnerabilities targeting IoT devices. AI technology can identify these attacks and prevent them from happening. AI is used by about 30{ddb1837796c24bf9e17c056f36036938e7ba6ab25bc534bc9155aeff21533e17} of chief information security officers (CISOs). While data breaches are a real danger, it’s important to remember that hackers are constantly developing new ways to exploit vulnerabilities.

Attacks on machine learning systems can be carried out at two different stages, namely at the inference stage and the training phase. In both cases, the attacker must have some knowledge about the model before probing the results. However, there are several ways to protect against such attacks. For example, input validity checking, rate limiting, regression testing, manual moderation, and various statistical techniques can prevent data poisoning attacks. Therefore, detecting data poisoning is essential in protecting the security of machine learning systems.

Predict Bad Loans

As a financial institution, you have a variety of products to sell. One of them is a line of credit. This allows you to earn interest on the money you loan to people. Consequently, the performance of these loans is vital to your bank’s profitability. To help reduce your Non-Performing Assets (NPA), you should try to predict which loans will default. Machine learning is an excellent tool to do this.

There are several ways to train machine learning models to predict the performance of loans. One of the most common techniques is the Logistic regression model, a statistical technique. To prepare your model, you can use Kaggle data, an open dataset that allows you to compare various predictive methods. The performance of each model is measured by its sensitivity and specificity. By comparing their versions, you can find the most effective model to predict defaults.

Speed Up Drug Discovery

There are several ways in which machine learning can help speed up drug discovery. Traditionally, drug discovery has been a long process involving vast screening libraries of compounds. Then, scientists must evaluate and test these compounds to identify the most promising ones. Increasingly, chemists rely on rational structure-based drug design, which avoids these initial screening steps. However, the sheer volume of drug libraries makes it difficult for individual researchers to review every compound. AI and machine learning can help by automating the screening process and identifying potential targets.

The algorithms created by machine learning can recognize subtle differences in how cells respond to different chemicals. For example, the researchers at NIBR recently used machine learning to predict the sound effects of drugs at different doses. This computerized network was almost 100 percent accurate in predicting the correct impacts of medications at different amounts. The results were encouraging, as the automated network accurately predicted the effects of other chemicals on cells. As a result, drug discovery can be accelerated, and drug development costs reduced.