Machine learning is the latest buzzword sweeping the business world around the world. It has captured people's imaginations and conjured visions of self-learning AI and robots of the future. Across industries, machine learning has paved the way for technological achievements and tools that were not possible just a few years ago. From prediction engines to online TV live streaming, machine learning drives the breakthrough innovations that underpin our modern lifestyles.
Now, before we discuss the various applications of Machine Learning, let us first understand what Machine Learning is.
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What is Machine Learning?
Machine learning is a collective term for a set of techniques and tools that help computers learn and adapt on their own. Machine learning algorithms enable AI to learn without being explicitly programmed to perform a desired action. By learning patterns from example inputs, machine learning algorithms predict and perform tasks based solely on learned patterns, not predefined program instructions. Machine learning is a lifesaver in many cases where strict algorithms cannot be applied. It learns new processes from previous patterns and executes on that knowledge.
One of the most familiar machine learning applications is how email providers deal with spam: Spam filters use algorithms to identify incoming unwanted emails and move them to the spam folder. Some e-commerce companies also use machine learning algorithms in combination with other IT security tools to prevent fraud and improve the performance of their recommendation engines.
Let’s explore other real-world machine learning applications that are taking the world by storm.
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Popular Machine Learning Applications and Examples
1. Social Media Features
Social media platforms use machine learning algorithms and approaches to create engaging and relevant features. For example, Facebook recognises and records your activity, chats, likes, comments and the time you spend on certain types of posts. Machine learning learns from your own experience and creates suggestions of friends and pages that are suitable for your profile.
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2. Product Recommendations
Product recommendations are one of the most popular and well-known applications of machine learning. Product recommendations are one of the prominent features of almost every e-commerce website today and are an advanced application of machine learning technology. Using machine learning and AI, websites track user behavior and recommend products based on previous purchases, search patterns, and cart history.
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3. Image Recognition
Image recognition, an approach to cataloging and detecting features and objects in digital images, is one of the most important and prominent machine learning and AI techniques. This technique is employed for further analysis such as pattern recognition, face detection, and facial recognition.
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4. Sentiment Analysis
Sentiment analysis is one of the most necessary applications of machine learning. Sentiment analysis is a real-time machine learning application that determines the emotions and opinions of a speaker or writer. For example, if someone writes a review or an email (or any form of document), the sentiment analysis tool instantly finds out the actual thoughts and tone of the text. This sentiment analysis application can be used to analyze review-based websites, decision-making applications, and more.
5. Automate Employee Access Control
Organizations are actively implementing machine learning algorithms to determine the level of access employees require in different areas depending on their job profile. This is one of the best applications of machine learning.
6. Protecting Marine Life
Machine learning algorithms are being used to develop behavioral models of endangered cetaceans and other marine species, helping scientists regulate and monitor their populations.
7. Medical Efficiency and Regulation of Health Services
Critical healthcare departments are actively considering the use of machine learning algorithms to improve their management. These models predict the waiting time of patients in the emergency waiting rooms of different departments of the hospital. The models use key factors that help define the algorithm, staff details at different times, patient records, complete logs of department chats, layout of the emergency room. Machine learning algorithms also help in disease detection, treatment planning, and prediction of disease status. This is one of the most wanted machine learning applications.
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8. Predict the likelihood of heart failure
Algorithms designed to scan doctors' free-form electronic notes and identify patterns in patients' cardiovascular histories are creating a buzz in the medical community. Instead of doctors having to sift through multiple medical records to arrive at an accurate diagnosis, computers can now perform analysis based on available information, reducing redundancy.
9. Banking Domains
Banks are now leveraging the latest advancements in machine learning to prevent fraud and protect their accounts from hackers. Algorithms determine the factors that need to be considered to create filters to prevent damage. Various fraudulent sites are automatically filtered and restricted from initiating transactions.
10. Language Translation
One of the most common uses of machine learning is language translation. Machine learning plays a vital role in translating from one language to another. We are amazed at how easily websites can translate from one language to another and provide contextual meaning as well. The technology behind translation tools is called “machine translation”. It has enabled people to interact with people all over the world. Without machine translation, life would not be as easy as it is today. Machine translation allows travelers and business people to safely step into foreign countries with the confidence that language is no longer a barrier.
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A model needs to be taught what you want it to learn. Relevant data can be fed back to help the machine draw patterns and act accordingly. To enable the machine to learn as expected, it is essential to provide it with relevant data and feed files. In this case, with machine learning, the desired outcome depends on the contents of the files recorded.
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Applications of Unsupervised Learning
Unsupervised learning allows systems to use AI algorithms to identify patterns in datasets that have not been labeled or classified. There are many examples of unsupervised learning, but some common ones are recommender systems, product segmentation, dataset labeling, customer segmentation, and similarity detection.
Applications of Reinforcement Learning
Reinforcement learning is also commonly used in many different types of machine learning applications. Some common application examples of reinforcement learning are industrial automation, self-driving car technology, applications that use natural language processing, and robotics. Reinforcement learning is used in AI across a wide range of industries, including finance, healthcare, engineering, and gaming.
Machine Learning for Algorithmic Trading
Algorithms are a key part of machine learning, a technology that relies on patterns and rules in data to achieve a specific goal or accomplish a specific task. When it comes to machine learning for algorithmic trading, key data is extracted to automate or support essential investment activities. Examples include successfully managing a portfolio, making decisions about buying and selling stocks, etc.
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Conclusion
Popular machine learning applications and technologies are evolving rapidly, and we are excited about the possibilities our AI course offers in the future. As the demand for AI and machine learning increases, organizations need professionals with thorough knowledge and practical experience in these growing technologies. With this essential need in mind, Simplilearn has partnered with IBM to launch an AI and ML course with Purdue University. The course will equip you with skills and technology expertise across a range of industries, from Python, NLP, and speech recognition to advanced deep learning. This graduate program will help you stand out and advance your career in the vibrant fields of AI, machine learning, and deep learning.
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