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Machine Learning Tutorial
  • Basic
    • Introduction To Machine Learning
    • What is Machine Learning?
    • Uses of Machine Learning
    • Applications of Machine Learning
    • Naive Bayes in Machine Learning
    • Dataset Labelling
    • DataSet Example
    • Deep Learning Techniques
    • Dataset ZFS
    • Careers in Machine Learning
    • What is Machine Cycle?
    • Machine Learning Feature
    • Machine Learning Programming Languages
    • What is Kernel in Machine Learning
    • Machine Learning Tools
    • Machine Learning Models
    • Machine Learning Platform
    • Machine Learning Libraries
    • Machine Learning Life Cycle
    • Machine Learning System
    • Machine Learning Datasets
    • Machine Learning Certifications
    • Machine Learning Python vs R
    • Optimization for Machine Learning
    • Types of Machine Learning
    • Machine Learning Methods
    • Machine Learning Software
    • Machine Learning Techniques
    • Machine Learning Feature Selection
    • Ensemble Methods in Machine Learning
    • Support Vector Machine in Machine Learning
    • Decision Making Techniques
    • Restricted Boltzmann Machine
    • Regularization Machine Learning
    • What is Regression?
    • What is Linear Regression?
    • Dataset for Linear Regression
    • Decision tree limitations
    • What is Decision Tree?
    • What is Random Forest
  • Algorithms
    • Machine Learning Algorithms
    • Apriori Algorithm in Machine Learning
    • Types of Machine Learning Algorithms
    • Bayes Theorem
    • AdaBoost Algorithm
    • Classification Algorithms
    • Clustering Algorithm
    • Gradient Boosting Algorithm
    • Mean Shift Algorithm
    • Hierarchical Clustering Algorithm
    • Hierarchical Clustering Agglomerative
    • What is a Greedy Algorithm?
    • What is Genetic Algorithm?
    • Random Forest Algorithm
    • Nearest Neighbors Algorithm
    • Weak Law of Large Numbers
    • Ray Tracing Algorithm
    • SVM Algorithm
    • Naive Bayes Algorithm
    • Neural Network Algorithms
    • Boosting Algorithm
    • XGBoost Algorithm
    • Pattern Searching
    • Loss Functions in Machine Learning
    • Decision Tree in Machine Learning
    • Hyperparameter Machine Learning
    • Unsupervised Machine Learning
    • K- Means Clustering Algorithm
    • KNN Algorithm
    • Monty Hall Problem
  • Supervised
    • What is Supervised Learning
    • Supervised Machine Learning
    • Supervised Machine Learning Algorithms
    • Perceptron Learning Algorithm
    • Simple Linear Regression
    • Polynomial Regression
    • Multivariate Regression
    • Regression in Machine Learning
    • Hierarchical Clustering Analysis
    • Linear Regression Analysis
    • Support Vector Regression
    • Multiple Linear Regression
    • Linear Algebra in Machine Learning
    • Statistics for Machine Learning
    • What is Regression Analysis?
    • Clustering Methods
    • Backward Elimination
    • Ensemble Techniques
    • Bagging and Boosting
    • Linear Regression Modeling
    • What is Reinforcement Learning
  • Classification
    • Kernel Methods in Machine Learning
    • Clustering in Machine Learning
    • Machine Learning Architecture
    • Automation Anywhere Architecture
    • Machine Learning C++ Library
    • Machine Learning Frameworks
    • Data Preprocessing in Machine Learning
    • Data Science Machine Learning
    • Classification of Neural Network
    • Neural Network Machine Learning
    • What is Convolutional Neural Network?
    • Single Layer Neural Network
    • Kernel Methods
    • Forward and Backward Chaining
    • Forward Chaining
    • Backward Chaining
  • Deep Learning
    • What Is Deep learning
    • Overviews Deep Learning
    • Application of Deep Learning
    • Careers in Deep Learnings
    • Deep Learning Frameworks
    • Deep Learning Model
    • Deep Learning Algorithms
    • Deep Learning Technique
    • Deep Learning Networks
    • Deep Learning Libraries
    • Deep Learning Toolbox
    • Types of Neural Networks
    • Convolutional Neural Networks
    • Create Decision Tree
    • Deep Learning for NLP
    • Caffe Deep Learning
    • Deep Learning with TensorFlow
  • RPA
    • What is RPA
    • What is Robotics?
    • Benefits of RPA
    • RPA Applications
    • Types of Robots
    • RPA Tools
    • Line Follower Robot
    • What is Blue Prism?
    • RPA vs BPM
  • Interview Questions
    • Deep Learning Interview Questions And Answer
    • Machine Learning Cheat Sheet

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Applications of Machine Learning

By Priya PedamkarPriya Pedamkar

Application machine learning

Introduction to Applications of Machine Learning

Artificial Intelligence is a very popular topic which has been discussed around the world.  Machine learning is one of the most exciting technologies of AI that gives systems the ability to think and act like humans. machine learning is a subfield of AI  and has its various application which helps to make a prediction, analysis, classification, etc. that is recognized by the companies across several industries(like Financial Service, Government, Healthcare, Transportation, etc.) that deal with huge volumes of data needed by the organizations in running their business effectively and to get an edge over their competitors.

Applications based on Line of Business

Let’s categorized the uses of machine learning based on the line of business.

1. Manufacturing

As an Industry, Manufacturing is the backbone of any healthy economy.From optimized resource planning to cut short the time to market, Machine learning is helping the transformation of the manufacturing sector.

2. Marketing

In a world of 25 billion-plus connected devices, machine learning plays a vital role in personalized digital marketing. Ads click prediction, showing relevant Ads to customers, identifying target customers, churn analysis, etc., are important applications of machine learning in the marketing sector.

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3. Healthcare

Healthcare is probably the sector where the impact of artificial intelligence will be miraculous. As a sector historically, healthcare is highly dependent on manual intervention and highly skilled professionals. But in today’s world, machine learning enables us to make data-driven decisions that can prevent diseases, helps in better patient diagnosis, faster root cause detection, etc. Tech giants Google, Facebook, Qualcomm, etc., are investing billions in ML-based healthcare research.

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4. Digital Media and Entertainment

Machine learning has tremendous applications in digital media, social media and entertainment. Personalized recommendation (i.e. Youtube video recommendation), user behavior analysis, spam filtering, social media analysis, and monitoring are some of the most important applications of machine learning.

5. E-commerce

Advancements in machine learning is also a key stakeholder in today’s e-commerce transformation. When we are browsing an e-commerce site, we can see personalized recommendations, which is achieved through content-based or collaborative filtering. The availability of large-scale user data is probably what keeps e-commerce giants ahead in the race than retailers. Machine learning is also used in fashion designing.Indian E-Commerce giant Myntra has multiple brands that are designed by deep learning systems.

6. Energy

Energy is one of the core sectors where machine learning solutions are bringing huge differences. Power consumption and requirements prediction, dynamic per unit cost maintenance, hardware lifespan analysis are part of machine learning applications in this sector.It is also being used for managing alternate energy resources.

7. Banking and Financial

In a digital economy, machine learning helps banks and other financial organizations to safeguard from frauds, money laundering, illegal financial detection, identifying valuable customers, etc. It also helps financial organizations with stock market predictions, demand forecasting, offering personalized banking solutions to the customers, etc.

8. Automobile

An automobile is another sector where the impact of machine learning is huge. Almost every automobile manufacturer uses artificial intelligence to optimise fuel consumption, breakdown prediction, and even self-driving. Tesla, Nvidia, etc., are investing a lot over self-driving cars.

9. Customer Service

Almost every organization is using chatbots for customer services. Chatbots are cost-effective and changing the customer service landscape to a large extent. Automated translation and state-of-the-art text-to-speech and speech to text systems are helping to overcome the language barrier.

10. Governance and Surveillance

Machine learning is reshaping modern Governance and defense systems. With the help of the state of the art deep learning algorithms and infrastructures, security agencies are now enabled with real-time image detection, drone surveillance, automated social network monitoring, etc.

11. Insurance

Insurance is sitting on a gold mine of data that is traditionally being used only at the application level as an industry. With the help of artificial intelligence and machine learning, Insurers are now empowered with valuable insights from the data they possess. Machine learning is being used for faster claims recovery, fraud detection, renewal prediction, churn analysis, etc. From New new business today two transactions, it can be used at every stage of the policy life cycle.

12. Human Resource Management

Though it is at an early age, machine learning is now also being used to manage human resources. Organizations like Amazon, HDFC bank, etc., are using bots and video analytics at various phases of their recruitment process. IBM Watson is also used for human resource optimization.

13. Transportation

While using app cab rides, you must have observed the dynamic pricing and surge charges at some point in time. This is also an application of machine learning. User data is also being used to predict the shortest path.

14. Art and Creativity

Machine learning is no longer being used to automate the mundane jobs for humans; it is also being used for creative purposes. Artistic style transfer, text to image synthesis, automated soundtrack and video creation, image colouring, social media chatbots, etc., are some of the cool applications of machine learning in this sector.

Trends in Machine Learning

From the beginning of the internet era, the applications of machine learning are increasing exponentially. Let’s look at the world wide google trends for machine learning for the period of 2004 to 2019.

trend in machine learning

Source: https://trends.google.com

Conclusion

Machine learning and artificial intelligence are no longer science fiction or part of Hollywood movies; their applications are everywhere in our day to day life. Every innovation has a positive and negative side; machine learning is also not an exception. Though in this article, we discussed mainly the positive applications of machine learning, it can also be used as evil. Deep learning systems like Deep Fakes have a huge impact on human life and privacy. As a growing field of study and applications, the need for strong data governance is also emerging as a necessity.

Recommended Articles

This is a guide to the Applications of Machine Learning. Here we discuss on Applications based on Line of Business and Trends in Machine Learning. You can also go through our other related articles to learn more –

  1. Machine Learning Platform
  2. Machine Learning Techniques
  3. Uses Of Machine Learning
  4. Introduction To Machine Learning
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