Humans learn from experience and so do machines (all thanks to machine learning). The only difference is that machines are 100 times more accurate and faster. For now, let’s skip the “but humans are the ones who made machines” debate and focus on machine learning (ML). Machine learning is the technology that enables applications to improve their decision-making abilities on their own. To achieve this, the applications are fed with massive amounts of data inputs to recognize certain patterns. Let’s dive into the top 5 machine learning applications to better understand the implementation of machine learning technology.
Top 5 Machine Learning Applications
1.) Recommendation Systems
Recommendation systems are machine learning algorithms that recommend the most relevant results to users based on their preferences, interests, and behavior (how a user interacts with a website). Product recommendation systems are amongst the most popular machine learning applications. E-commerce sites like Amazon use recommendation systems to suggest users their most likely purchase based on their purchase history, cart details, website movement, etc.
You are browsing a website, and a window pops up on your screen asking, ‘How may I help you?’. Recently, your neighborhood cafe has installed a cool-tech that allows you to place your order using voice commands. Wait, how can you forget getting your queries solved within minutes on a live chat? Well, all these are examples of scenarios where you encountered a chatbot.
In simple words, a chatbot is a machine learning tool that provides conversational responses to user queries in a human-like fashion. For this, chatbots make use of automated rules, natural language processing (NLP, the ability of an algorithm to read, understand, and derive meaning from human languages), machine learning (ML), and artificial intelligence (AI).
3.) Sentiment Analysis
Say you have an e-commerce store with thousands of reviews on several products. Reading these many reviews to study customer feedback can be a nightmare. Don’t worry; sentiment analysis has got your back. Sentiment analysis is a machine learning application that automatically analyzes a piece of text and determines whether the sentiment behind it is positive, negative, or neutral. To achieve this, sentiment analysis uses machine learning and natural language processing (NLP). Analyzing customer feedback is a popular use case of sentiment analysis. For this, customer feedback from mediums like Twitter, Facebook, etc. is collected and is subjected to sentiment analysis algorithms.
4.) Image Recognition
You hold your phone, and within seconds it recognizes you and gets unlocked. Today, you may take this for granted but believe it or not, image recognition is by far the most fascinating application of machine learning. Image recognition is a machine’s ability to identify objects, places, people, etc.
5.) Fraud Detection
To combat fraud, machine learning models are fed with transaction data and are trained to identify certain characteristics to differentiate fraudulent and legitimate transactions. Based on this data, whenever a user initiates a transaction, the machine learning algorithm scans the profile for suspicious patterns. If the transaction is legitimate, it gets a green signal.
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