What’s the difference between Machine Learning & Deep Learning?

Machine learning and deep learning are two subfields of artificial intelligence that are used to teach computers how to make predictions or decisions based on data. While both use algorithms to analyze data and learn from it, there are some key differences between the two.

Machine learning is a type of artificial intelligence that uses algorithms to identify patterns and make predictions based on data. It involves training a model on a dataset and then using that model to make predictions on new data. Machine learning algorithms can be supervised, unsupervised, or semi-supervised, and they are often used in a wide range of applications, such as image recognition, natural language processing, and fraud detection.

ML&DL

Deep learning, on the other hand, is a subset of machine learning that uses artificial neural networks to model and solve complex problems. It involves building a neural network with multiple layers that can learn and make decisions based on data. Deep learning is particularly useful in applications such as image and speech recognition, natural language processing, and autonomous vehicles.

• The main difference between deep learning and machine learning is due to the way data is
  presented in the system. Machine learning algorithms almost always require structured data,
  while deep learning networks rely on layers of ANN (artificial neural networks).
• Machine learning algorithms are designed to “learn” to act by understanding labeled data and
  then use it to produce new results with more datasets. However, when the result is incorrect,
  there is a need to “teach them”. Because machine learning algorithms require bulleted data, they
  are not suitable for solving complex queries that involve a huge amount of data.
• Deep learning networks do not require human intervention, as multilevel layers in neural
  networks place data in a hierarchy of different concepts, which ultimately learn from their own
  mistakes. However, even they can be wrong if the data quality is not good enough.
• Data decides everything. It is the quality of the data that ultimately determines the quality of the
  result.

Machine Learning vs Deep Learning

In summary, machine learning is a broad category of algorithms that can be used to teach computers how to make predictions or decisions based on data, while deep learning is a specific subset of machine learning that involves building and training neural networks to model and solve complex problems.

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