New Computer World

BASIC DIFFERENCE BETWEEN MACHINE LEARNING AND DEEP LEARNING

Machine learning and deep learning – Most people tend to confuse machine learning and deep learning. Let’s understand what machine learning is and what deep learning is to clear the confusion.

Machine Learning

Machine learning is a branch of computer science. It provides algorithms to the machinery to have the ability to run and learn themselves from the data already present in it. Machine learning uses information based on past experiences to find accurate results. We can say that machine learning is a subset of Artificial Intelligence.

Deep Learning

Deep learning is similar to machine learning; however, it has numerous layers of networks where each layer consists of algorithms. The algorithms of deep learning can learn themselves without any human intervention. To understand more about them, you can join a data science certification course. Such certification helps to attain adequate theoretical and practical knowledge.

Difference Between Machine Learning and Deep Learning

Human Involvement:

  • In the algorithms used in machine learning, a person needs to identify and personally hand code the applied features based on the type of data. However, there is no need for human intervention in deep learning as it works with neural networks. Neural networks interpret like a human brain. The neural networks learn and modify the algorithms with the collected data over time.
  • Machine learning algorithms must learn to process by understanding the data and then using the same data to produce new results. Still, if the results are wrong, then human intervention is necessary. Thus, once you understand what is machine learning, you also understand its pros and cons. There is no need for human intervention in deep learning networks as the multi-level layers in the neural networks place the data in a hierarchy of different concepts. Through this way, they learn from their own mistakes, without any intervention from anyone. Keep in mind; the algorithms can be wrong if the quality of the data is not good enough.

Internal Structure and Working:

  • While machine learning represents the data in the form of structured data, deep learning uses the ANN, i.e., Artificial Neural Networks. In ANN, each neural network gives certain results and feeds them into the next layers as input. At last, the algorithm decides the results from the given output. To understand more about these algorithms or neural networks, you can always join a data science certification course from any reputed institute.
  • Machine learning uses different automated algorithms that turn to model functions and predict future action from the data. At the same time, deep learning uses neural networks that pass the data through all the processing layers to interpret the data features and relations.
  • Machine learning consists of numerous data. Thus it cannot be used for complex problems. However, deep learning consists of numerous data points and has multi-level layers and hierarchies, due to which many complex problems can be solved using deep learning.

There is no doubt that machine learning, data learning, and artificial intelligence are some of the top emerging technologies nowadays. Many professionals in this field are getting a pretty good package due to its demand. Learning about AI and other essential programs can take your career to a new height.

 

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