Artificial Intelligence (AI) is getting more and more common. It’s possible that you’re already using it without even realizing it. One of the most common uses of AI is Machine Learning (ML), in which computers, software, and devices accomplish tasks using cognition. A few machine learning examples that we utilize on a regular basis are listed below. But first, let’s discuss what machine learning is and how it works.
What is Machine Learning?
Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic how humans learn in order to improve accuracy over time. It’s a crucial component of the rapidly growing field of data science. In data mining projects, algorithms are taught to provide classifications or predictions using statistical methodologies, exposing key insights. Then, with the purpose of influencing crucial growth KPIs, these insights drive decision-making within applications and companies.
The Importance of Machine Learning: Healthcare, banking, infrastructure, marketing, self-driving cars, recommendation systems, chatbots, social media sites, gaming, cyber security, and many other sectors use machine learning techniques. Machine Learning is still in its infancy, with a slew of new technologies being added all the time. It helps us in a variety of ways, including analyzing large amounts of data, extracting data, and interpreting results. That’s why Machine Learning can be used in an unlimited number of ways.
Top 5 Uses Of Machine Learning:
- Image recognition: Image recognition is one of the most common applications of machine learning. It’s used to identify individuals, places, and digital images, among other things. Automatic buddy tagging suggestion is a typical application of facial recognition and image recognition. Facebook offers a feature that suggests friend auto-tagging. We get an automatic tagging recommendation with their names when we submit a photo with our Facebook friends, which is driven by machine learning’s face identification and recognition algorithm.
- Online Fraud Detection: Machine learning makes our online transactions safer and more secure by detecting fraud activities. There are several ways for a fraudulent transaction to occur when we execute an online transaction, including the use of phony accounts, fake ids, and the theft of funds in the middle of a transaction. The Feed Forward Neural Network supports us in detecting this by identifying whether the transaction is legitimate or fraudulent. Each legitimate transaction’s output is converted into hash values, which are subsequently utilized as input for the next round. Each authentic transaction has a unique pattern that differs from a fraud transaction, allowing it to be detected and making our online transactions safer.
- Product recommendations: Machine learning is used by a number of e-commerce and entertainment companies, like Amazon, Netflix, and others, to offer product recommendations to users. Because of machine learning, every time we search for a product on Amazon, we begin to see adverts for the same products while using the same browser. Google deduces the user’s interests and recommends products based on those interests using a variety of machine learning algorithms. Similarly, when we use Netflix, we receive machine-learning-based recommendations for entertainment series, movies, and other stuff.
- Automatic Language Translation: Visiting a new region and not understanding the language is no longer a problem; machine learning can assist us with this by converting text into our native languages. Google’s GNMT (Google Neural Machine Translation) provides this capability, which is a Neural Machine Learning that automatically converts text into our native tongue. The technology behind the automatic translation is a sequence learning process that is combined with picture recognition and translates text from one language to another.
- Traffic Predictions: We’ve all utilized GPS navigation services. Our current locations and speeds are kept on a central server for traffic control while we’re doing that. After that, the data is used to produce a traffic map. While this helps with traffic prevention and analysis, the underlying problem is that the number of GPS-equipped cars is restricted. Machine learning can help in these situations by estimating the locations where congestion can be discovered based on everyday interactions.
- Videos Surveillance: Artificial intelligence (AI) is used in today’s video surveillance systems to detect crimes before they happen. They keep track of unusual behavior like people standing still for long periods of time, stumbling, and dozing on benches, among other things. As a result, the system can alert human passengers, potentially avoiding collisions. When these types of activities are reported and validated, it helps to improve surveillance services. This is due to the background processing of machine learning.
- Email Spam and Malware Filtering: Spam filters are used by email clients in a variety of ways. These spam filters are updated on a daily basis thanks to machine learning. The adoption of rule-based spam filtering fails to keep up with spammers’ latest methods. Multi-Layer Perceptron and C 4.5 Decision Tree Induction are two spam filtering techniques enabled by machine learning. Every day, approximately 325,000 malware are detected, with 90–98% of each piece of code mimicking its prior forms. The coding pattern is recognized by machine learning-powered system security programs. As a result, they can quickly detect and guard against new infections with a 2–10 percent variance.
Final Thoughts: The tech industry will be ruled by machine learning. That’s why learning ML can assist you in obtaining a stable position. There are many tutorial blogs and videos available to get you started. YouTube is the best place to learn because it is free. Use Career Ninja‘s Learn Tube chrome extension for hand-holding training on YouTube. Learn Tube organizes the results of your YouTube search into a course framework. If you want to learn “How to learn ML from scratch”, search that term on youtube and Learn Tube will show you a bunch of videos like an online course. As a beginner, you’ll click through the videos from the first to the last, as if you were taking an online course tailored specifically for you on YouTube.
