Machine Learning vs Deep Learning

Machine Learning vs Deep Learning

What Do You Mean by Machine Learning?

Machine learning is the field of artificial intelligence. It pays attention to the algorithms and statistical models that enable computers to make predictions without being programmed. Machine learning involves the training of algorithms on large datasets as it helps identify patterns. The computer can make informed decisions about the new data through these patterns.

Types of Machine Learning

Machine learning consists of 2 categories based on the data. They are:

  • Supervised learning: It helps in training data and labels for the correct answers.
  • Unsupervised learning: During this learning, the main motto remains to find the patterns in the datasets at hand. There are no particular labels in this dataset.

What Do You Mean By Deep Learning?

Deep learning is a subset of machine learning. It uses neural networks to analyze the relationships and patterns in data. This is structured like the functioning of a human brain. It completes different tasks and has natural language processing and speech recognition features as well. This model is used to train massive amounts of algorithms and data. It ensures more accuracy during the processing time. It is suitable for real-world and complex problems and enables them to learn new situations.

What is the Future of Machine Learning and Deep Learning?

Both learning models have the potential to transform industries in a good way. They have diverse applications in industries including transportation, retail, finance, and healthcare. Machine learning uses data to give accurate and real results in less time. They can learn and improve their work without being programmed to that level. Deep learning solves complex problems with the help of algorithms and has many more levels of algorithms.

Difference Between Machine Learning And Deep Learning?

Certain features make machine learning different from deep learning. Here are some differences:

  • Machine learning is considered as a superset of deep learning, whereas, deep learning is the subset of machine learning.
  • The data shown in machine learning is in a structured format while in deep learning it is based on neural networks.
  • Machine learning is the evolution of artificial intelligence and deep learning is the evolution of machine learning.
  • Machine learning has hundreds and thousands of data points while the latter has more than millions of data points.
  • The outputs given by machine learning are in numerical value while in deep learning the output is given between numerical values, texts and sound.
  • Machine learning uses different forms of automated algorithms that can predict future action from data. Deep learning uses neural networks through processing layers to understand the relationships and patterns of data.
  • In machine learning the algorithms are detected by data analysts and in deep learning they are self-depicted on data analysis after they are put into production.
  • There has been a high demand for machine learning as it helps in learning new things. Deep learning focuses on solving complex issues through their respective methods.
  • The CPU can be used to perform training in machine learning. Deep learning required a dedicated Graphics Processing Unit for training.
  • Machine learning has more human interference for obtaining real results while deep learning requires less human interference after it is processed.
  • Machine learning can easily be set up and run while deep learning requires more time to set up. Although it gives immediate results.
  • Less time in training is required due to its small size in machine learning. In deep learning, there are big data points, so it requires more time.
  • The feature engineering in machine learning is done by humans whereas deep learning is not required as it can be detected by neural networks automatically.
  • It is simpler and can be executed on standard computers. Deep learning systems need powerful hardware and more resources.
  • The results given by machine learning are easier to explain than deep learning.
  • Machine learning models can be used to solve easy and little tough issues however deep learning machine models can be used for resolving challenging issues.
  • Machine learning models doctor’s clinics, banks and mailboxes while deep learning models include self-driving automobiles and surgical robots.
  • Machine learning algorithms range from simple linear models to complex models. Deep learning algorithms are based on artificial neural networks that consist of multiple layers.
  • In machine learning models the data requirement is lesser as they focus on the quality of the data. Deep learning algorithms require large amounts of data and they have their way of processing data.
  • Machine learning can be used for regression, clustering and classification. Deep learning can be used in image and speech recognition, natural language processing and autonomous systems.

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