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Machine Learning – Applications

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  • Difficulty Level : Easy
  • Last Updated : 26 Jan, 2023
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Introduction

Machine learning is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that which makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect. We probably use a learning algorithm dozens of time without even knowing it. Applications of Machine Learning include:

  • Web Search Engine: One of the reasons why search engines like google, bing etc work so well is because the system has learnt how to rank pages through a complex learning algorithm.
  • Photo tagging Applications: Be it facebook or any other photo tagging application, the ability to tag friends makes it even more happening. It is all possible because of a face recognition algorithm that runs behind the application.
  • Spam Detector: Our mail agent like Gmail or Hotmail does a lot of hard work for us in classifying the mails and moving the spam mails to spam folder. This is again achieved by a spam classifier running in the back end of mail application.
  • Augmentation:Machine learning, which assists humans with their day-to-day tasks, personally or commercially without having complete control of the output. Such machine learning is used in different ways such as Virtual Assistant, Data analysis, software solutions. The primary user is to reduce errors due to human bias.
  • Automation:Machine learning, which works entirely autonomously in any field without the need for any human intervention. For example, robots performing the essential process steps in manufacturing plants.
  • Finance Industry:Machine learning is growing in popularity in the finance industry. Banks are mainly using ML to find patterns inside the data but also to prevent fraud.
  • Government organization:The government makes use of ML to manage public safety and utilities. Take the example of China with the massive face recognition. The government uses Artificial intelligence to prevent jaywalker.
  • Healthcare industry: Healthcare was one of the first industry to use machine learning with image detection.
  • Marketing:Broad use of AI is done in marketing thanks to abundant access to data. Before the age of mass data, researchers develop advanced mathematical tools like Bayesian analysis to estimate the value of a customer. With the boom of data, marketing department relies on AI to optimize the customer relationship and marketing campaign.

Today, companies are using Machine Learning to improve business decisions,increase productivity, detect disease, forecast weather, and do many more things. With the exponential growth of technology, we not only need better tools to understand the data we currently have, but we also need to prepare ourselves for the data we will have. To achieve this goal we need to build intelligent machines. We can write a program to do simple things. But for most of times Hardwiring Intelligence in it is difficult. Best way to do it is to have some way for machines to learn things themselves. A mechanism for learning – if a machine can learn from input then it does the hard work for us. This is where Machine Learning comes in action. Some examples of machine learning are:

  • Database Mining for growth of automation: Typical applications include Web-click data for better UX( User eXperience), Medical records for better automation in healthcare, biological data and many more.
  • Applications that cannot be programmed: There are some tasks that cannot be programmed as the computers we use are not modelled that way. Examples include Autonomous Driving, Recognition tasks from unordered data (Face Recognition/ Handwriting Recognition), Natural language Processing, computer Vision etc.
  • Understanding Human Learning: This is the closest we have understood and mimicked the human brain. It is the start of a new revolution, The real AI. Now, After a brief insight lets come to a more formal definition of Machine Learning
  • Arthur Samuel(1959): “Machine Learning is a field of study that gives computers, the ability to learn without explicitly being programmed.”Samuel wrote a Checker playing program which could learn over time. At first it could be easily won. But over time, it learnt all the board position that would eventually lead him to victory or loss and thus became a better chess player than Samuel itself. This was one of the most early attempts of defining Machine Learning and is somewhat less formal.
  • Tom Michel(1999): “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” This is a more formal and mathematical definition. For the previous Chess program
    • E is number of games.
    • T is playing chess against computer.
    • P is win/loss by computer.

 

Machine learning has many applications in a variety of fields. 

Some examples of areas where machine learning is used include:

  • Computer vision: Machine learning algorithms can be used to recognize objects, people, and other elements in images and videos.
  • Natural language processing: Machine learning algorithms can be used to understand and generate human language, including tasks such as translation and text classification.
  • Recommendation systems: Machine learning algorithms can be used to recommend products or content to users based on their past behavior and preferences.
  • Fraud detection: Machine learning algorithms can be used to identify fraudulent activity in areas such as credit card transactions and insurance claims.
  • Healthcare: Machine learning algorithms can be used to predict disease outbreaks, identify potential outbreaks, or predict patient outcomes.
  • Finance: Machine learning algorithms can be used to predict stock prices, identify fraudulent activity, or identify potential investment opportunities.

One simple python code:

Python3




from sklearn import tree
 
# Training data
X = [[140, 1], [130, 1], [150, 0], [170, 0]]  # [weight, texture] (0: smooth, 1: bumpy)
y = [0, 0, 1, 1# 0: apple, 1: orange
 
# Train a classifier
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, y)
 
# Make a prediction
prediction = clf.predict([[160, 0]])  # should return 1 (orange)
print(prediction)


output:

1

If you run the code I provided, the output will be the prediction made by the model. In this case, the output will be [1], indicating that the model predicts that the fruit with a weight of 160 and a smooth texture is an orange.

In the Next tutorial we shall classify the types of Machine Learning problems and shall also discuss about useful packages and setting environment for Machine Learning and how can we use it to design new projects.

There are many applications of machine learning, some examples include:

  1. Image and speech recognition
  2. Natural language processing
  3. Recommender systems
  4. Anomaly detection
  5. Fraud detection
  6. Predictive maintenance
  7. Robotics
  8. Self-driving cars
  9. Healthcare
  10. Financial services
  11. Marketing
  12. Agriculture
  13. Energy
  14. and many more.

References: 

[1] Machine Learning in action by Peter Harrington. [2] cs229.stanford.edu

There are many books available on the subject of machine learning applications. A few examples include:

“Applied Machine Learning” by Alaa Tharwat and Ahmed Abdel-Hamid, which provides an introduction to machine learning techniques and their applications in various fields such as computer vision, natural language processing, and bioinformatics.

“Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras” by Rajalingapuram Kannan and Rajalingapuram Kannan, which provides an in-depth introduction to deep learning techniques and their applications in computer vision.

“Machine Learning for Healthcare: Predictive models and algorithms” by Shonali Krishnaswamy and Shonali Krishnaswamy, which provides an overview of machine learning techniques and their applications in healthcare, including predictive modeling, data mining, and natural language processing.

“Practical Machine Learning for Computer Vision: Real-world applications and deep learning techniques” by David Salgado and David Salgado, which provides an introduction to machine learning techniques and their applications in computer vision and deep learning.

“Applied Machine Learning for Medical Imaging” by Nima Ghorbani, this book provides an overview of machine learning techniques and their applications in medical imaging and how to apply these techniques to real-world problems.

These are just a few examples, there are many other books available that cover different aspects of machine learning applications, so you may want to check out more options depending on your specific interests and needs.

This article is contributed by Abhishek Sharma. If you like GeeksforGeeks and would like to contribute, you can also write an article and mail your article to review-team@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.


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