So, as we discussed the basics of AI earlier that is "What is AI ?" and "What are its basic uses ?"
Generally, Artificial Intelligence is divided into:-
Machine Learning
Neural Networks
Evolutionary Computation
Computer Vision
Robotics
Expert Systems
Speech Processing
Natural Language Processing
Planning
Today we'll discuss "Machine Learning"
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
But, using the classic algorithms of machine learning, the text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.
Some Machine Learning Methods
Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
Reinforcement machine learning algorithms are a learning method that interacts with its environment by producing actions and discovering errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
Machine learning enables the analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information
Applications of Machine Learning
1. Image Recognition:
Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. The popular use case of image recognition and face detection is, Automatic friend tagging suggestion:
Facebook provides us a feature of auto friend tagging suggestions. Whenever we upload a photo with our Facebook friends, then we automatically get a tagging suggestion with a name, and the technology behind this is machine learning's face detection and recognition algorithm. It is based on the Facebook project named "Deep Face," which is responsible for face recognition and person identification in the picture.
2. Speech Recognition
While using Google, we get an option of "Search by voice," which comes under speech recognition, and it's a popular application of machine learning.
Speech recognition is a process of converting voice instructions into text, and it is also known as "Speech to text", or "Computer speech recognition." At present, machine learning algorithms are widely used by various applications of speech recognition. Google Assistants, Siri, Cortana, and Alexa are using speech recognition technology to follow voice instructions.
3. Traffic Prediction
If we want to visit a new place, we take the help of Google Maps, which shows us the correct path with the shortest route and predicts the traffic conditions. It predicts the traffic conditions such as whether traffic is cleared, slow-moving, or heavily congested with the help of two ways:
Real-Time location of the vehicle from Google Map app and sensors
Average time has taken on past days at the same time.
Everyone who is using Google Map is helping this app to make it better. It takes information from the user and sends it back to its database to improve its performance.
4. Product recommendations
Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc., for product recommendations to the user. Whenever we search for some product on Amazon, then we started getting an advertisement for the same product while internet surfing on the same browser, and this is because of machine learning.
Google understands the user interest using various machine learning algorithms and suggests the product as per customer interest.
As similar, when we use Netflix, we find some recommendations for entertainment series, movies, etc., and this is also done with the help of machine learning.
5. Self Driving Cars
One of the most exciting applications of machine learning is self-driving cars. Machine learning plays a significant role in self-driving cars. Tesla, the most popular car manufacturing company is working on self-driving cars. It is using an unsupervised learning method to train the car models to detect people and objects while driving.
Hence, in this article, we discussed briefly a major topic that comes under Artificial Intelligence. We'll discuss the others also.
Till then Stay Tuned Stay Safe
Comments