Machine Learning – How the world is using it?

machine learning

Table of Contents

Get an introduction to the interesting world of machine learning technology in this blog, which is a subfield of artificial intelligence (AI).

The robot-depicted world of the not-so-distant future relies on our ability to deploy artificial intelligence (AI) successfully. But transforming machines into thinking devices just like humans is not as easy as it may seem. Strong artificial intelligence (AI) can only be achieved with machine learning to help machines understand as humans do. In this blog, you will learn what is machine learning, its advantages and disadvantages, and real-world use cases. Let’s start! 

Machine Learning Definition

The term machine learning can be quite confusing. Hence, we must begin by clearly defining this term:

Machine learning (ML) is an application of artificial intelligence (AI). It enables systems to learn and improve from experiences without being programmed explicitly. It focuses on developing programs that can access data and use it to learn for themselves.

Pattern Recognition and Machine Learning

Pattern recognition is the technology that involves matching incoming data with information stored in a database. In other words, pattern recognition is the use of machine learning (ML) algorithms to identify patterns. Therefore, pattern recognition is a type of machine learning as it uses machine learning algorithms to recognize patterns.

Pattern recognition classifies data based on the knowledge gained from patterns and their representation or statistical information. This technique uses labeled training data to train pattern recognition systems.

Deep learning vs. Machine Learning

  1. Deep Learning is a subset of Machine Learning. Thus, Machine Learning is a superset of Deep Learning.
  2. The data represented in Machine Learning uses structured data and is different as compared to Deep Learning. The data representation used in Deep learning uses neural networks(ANN) and is quite different as compared to machine learning.
  3. Machine Learning is an evolution of AI, and Deep Learning is an evolution of Machine Learning. 
  4. The outputs of machine learning are numerical values, such as the classification of scores. The outputs of deep learning are anything from numerical values to free-form elements like free text and sound.
  5. Machine learning uses different types of automated algorithms. Deep learning uses a neural network that passes data through processing layers to interpret data features and relations.
  6. Machine learning algorithms are detected by data analysts. Deep learning algorithms are largely self-depicted on data analysis once they are put into production.
  7. Machine Learning is used to stay in the competition and learn new things. On the other hand, deep Learning solves complex machine learning issues.

Advantages and Disadvantages of Machine Learning

Advantages of machine learning

  • It is automatic
  • It is used in various fields
  • It can handle varieties of data
  • It has a huge scope of advancement
  • It can identify trends and patterns

Disadvantages of machine learning

  • It has more chance of error or fault
  • Data requirement is more
  • It is time-consuming and requires more resources
  • Inaccuracy of interpretation of data is high
  • More space is required

Real-world Use Cases of Machine Learning

  • Used by voice recognition software such as Siri or Alexa to detect words and intonation.
  • Used in dating sites to match people up based on interests.
  • Used at Amazon to find products you might want by searching through your shopping history.
  • Used by Netflix to suggest movies you might like watching.

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Uses of Machine Learning

Data Security

Machine learning models can identify data security vulnerabilities before they turn into breaches. Machine learning models can predict future risk activities by looking at past experiences so that high-risk can be proactively mitigated.

Finance

Fintech firms, banks, and trading brokerages use machine learning algorithms to provide financial advisory services to investors and automate trading. To automate customer support, Bank of America is using a chatbot called Erica, which runs on a machine learning algorithm.

Healthcare

Machine learning is used to analyze huge healthcare data sets to accelerate the discovery of treatments and cures, automate routine processes, improve patient outcomes, and prevent human error. For instance, IBM’s Watson uses data mining to provide physicians with data that they can use to personalize patient treatment.

Fraud Detection

Artificial intelligence is being used in the financial and banking sector to analyze large numbers of transactions autonomously to uncover fraudulent activity in real-time. Capgemini, a technology services firm, claims that fraud detection systems using machine learning algorithms and analytics improve detection accuracy by 90% and minimize fraud investigation time by 70%.

Retail

Artificial intelligence researchers and developers are using machine learning algorithms to develop AI recommendation engines offering relevant product suggestions based on buyers’ past choices and historical, geographic, and demographic data.

Embrace the power of Machine Learning

The contemporary world is becoming more data-driven. Hence, it becomes essential to analyze and systemize information that comes from multiple channels. Machine learning is a good choice here for structuring data comprehensively to make evidence-based decisions. 

If you wish to develop a machine learning project with kuenstlich-intelligent or have any questions on machine learning, get in touch with us today.

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