Deep Learning v/s Machine Learning: What’s the Difference?


In recent years, the subject that is known as artificial intelligence (AI) has increased in popularity. Deep learning and machine learning are two terms that are commonly used when discussing AI. While both methods are used in various AI applications, they’re different.  Although Deep Learning is a more advanced technique that utilizes artificial neural networks to create complicated patterns in data.

Machine Learning is a subset of AI that uses algorithms to train on labeled or unlabeled data in order to discover patterns and then generate predictions. In this article, we will share distinctions between deep and machine learning, along with their advantages and disadvantages will be covered throughout this post.

What is Machine Learning?

Machine learning (ML) algorithms are programmed to acquire knowledge from data and make judgments or predictions with no explicit programming. Machine learning is an element that is part of Artificial Intelligence (AI). Large amounts of data are automatically processed by ML algorithms to identify patterns and connections that permit the making of judgments or predictions that are based on new information. 

Based on the type of data used for training in supervised, unsupervised, and reinforcement learning techniques are divided into. When an algorithm is taught by analyzing data labeled in supervised learning, it develops patterns using unlabeled data for unsupervised learning. By trial-and-error, and receiving feedback through rewards or punishments the reinforcement learning process includes an algorithm that is learning.

Types of Machine Learning

Here we discussed different kinds of machine learning-

» Supervised Learning

Machine learning algorithms are taught using labelled data in supervised learning. It is a part of machine learning. The algorithm is trained with input data and output labels. The program can make predictions based on new data that is not explored after it has been taught.

» Unsupervised Learning

Unsupervised learning is one type of machine learning where an algorithm is taught using unlabeled data. The algorithm is expected to discover independently connections and patterns in the data. Unsupervised learning involves processes such as clustering and association.

» Reinforcement Learning

Machine learning algorithms are taught to make decisions in a given environment through interaction with it using the method called reinforcement learning. To maximize its benefits it learns from its mistakes and adapts its behavior in line with its mistakes.

What is Deep Learning?

Artificial neural networks (ANNs) are utilized for Deep Learning, a subset of Machine Learning, to model complex patterns in data. Through layers of interconnected nodes that analyze information and identify relevant aspects, they are designed to recreate the structure and function that the brain of a human. Deep Learning algorithms can handle unstructured data, such as images videos, audio, or videos, and are able to automatically extract relevant characteristics from the raw data, eliminating the requirement to manually engineer features. This makes them suitable for use in the natural processing of languages such as autonomous driving, and recognition of audio and image. While deep learning algorithms require a lot of computational power and an enormous amount of data in order to train and improved upon, they are far superior to traditional algorithmic machine learning in efficiency.

Types of Deep Learning

Here we discussed below different types of deep learning 

» Neural Networks

The neural networks constitute the basic components of Deep Learning. They are based on the way the human brain operates and is structured. Neurons, which are the interconnected nodes that form neural networks are able to process information. Every neuron receives input, executes a mathematical calculation on it, then outputs.

» Convolutional Neural Networks

Convolutional neural networks (CNNs) can be described as a form of neural network designed to recognize video and image images. The human visual cortex is used as their basis. CNNs can detect automatically the details such as edges or textures as well as shapes in videos and photos.

» Recurrent Neural Networks

For data that is sequenced, like audio and text, recurrent neural network (RNNs) constitute a kind of neural network used. They can recall the inputs of the past and make predictions for the input to come by using the information. Recognition of speech and sentiment and language translation are the most popular applications of RNNs.

Advantages and disadvantages of Machine Learning


» Speed: Machine learning is crucial in jobs such as fraud detection, speech, and picture recognition since it is able to quickly analyze and process huge quantities of information.

» Scalability: Machine learning algorithms are suitable for applications that require the processing of huge amounts of data since they are able to expand to handle huge datasets.

» Accuracy: Machine learning algorithms can be able of generating forecasts and results which are extremely precise.


» Limits to data available: In order to create predictions machine learning algorithms rely on the information. The results generated by the algorithm could be inaccurate or not reliable in the event that the data is biased or lacking.

» Overfitting: When machine-learning algorithms are able to overfit the dataset they are training on, they perform great on the data, but fail on fresh untested data.

» Needs expert technical knowledge: Machine Learning algorithms development and implementation requires specific technical skills and expertise.

Advantages and disadvantages of Deep Learning


» High accuracy: Deep Learning systems can produce extremely precise results and forecasts for complex issues.

» Automated feature extraction: Deep Learning algorithms are capable of automatically extracting relevant features from raw data, removing the requirement to manually engineer features.

» Improved performance for the unstructured data: Deep Learning algorithms are capable of processing unstructured data, such as images or videos as well as audio, and are suitable for use in areas like the identification of images and voices.


» Requires large amounts of data: The large volumes of data are essential to train Deep Learning algorithms. The algorithm’s performance may not be as effective when the data is not complete or is distorted.

» Computationally expensive: Deep Learning techniques are computationally costly because they require robust technology, such as GPUs for training and function correctly.

» “Black box” nature: The complex structures and operations associated with deep learning algorithms may make them difficult to study and understand.

Difference Between Machine Learning and Deep Learning

  Deep Learning Machine Learning
Definition Subset of AI that involves training algorithms on labeled or unlabeled data to learn patterns and make predictions Subset of ML that involves artificial neural networks to model complex patterns in data
Data Typically works with structured data Can work with structured and unstructured data, such as images, videos, and audio
Feature Engineering Requires manual feature engineering to extract relevant features from data Can automatically extract relevant features from raw data
Model Complexity Typically uses simpler models, such as decision trees or logistic regression Uses complex models, such as deep neural networks, that require significant computational resources
Training Data Requires a large amount of labeled data to train effectively Requires an even larger amount of labeled data to train effectively
Performance Can achieve high accuracy for certain tasks, such as classification or regression Can achieve higher accuracy than traditional ML algorithms for complex tasks, such as image and speech recognition
Interpretability Models can be easier to interpret, as they rely on simple algorithms Models can be harder to interpret, as they rely on complex neural networks

Machine learning and deep learning are two distinct types of artificial intelligence but they are very different in regard to performance, application, and methods. In contrast, while Deep Learning can handle unstructured information such as photos, videos, and audio recordings, Machine Learning is typically used for structured data like text.

The efficiency of Deep Learning algorithms has significantly over the performance of traditional Machine Learning algorithms for applications such as image identification and processing of natural languages but they are also more complex and require superior hardware.

In addition in contrast to Machine Learning algorithms, which require human skills to identify and extract relevant characteristics from datasets, Deep Learning algorithms do not necessarily require feature engineering.

Use Cases

Machine Learning:

» Fraud detection: Machine learning algorithms analyze transaction data and detect suspicious behavior patterns.

» Personalized recommendations: Requests for advice specific to the preferences of the user and behavior are achievable with algorithms for machine learning after an analysis of the user’s data.

» Speech recognition: Machine learning algorithms convert spoken words to text, by recognizing and categorizing them.

Deep Learning:

» Recognition of images: Deep Learning algorithms are crucial for applications such as self-driving cars and facial recognition since they are able to recognize elements and other features in images.

» Natural Language Processing (NLP): Deep Learning algorithms can analyze and understand natural language making them useful in tasks such as sentiment analysis as well as translation of language.

» Speech Synthesis: Deep Learning algorithms are capable of creating a synthetic speech that sounds human-like and natural.


There are a variety of applications for AI subsets referred to in the context of Deep Learning and Machine Learning. Although Deep Learning uses artificial neural networks to study complex trends in information, Machine Learning offers a simpler method that teaches algorithms to be trained on labeled or unlabeled data. Each method has its advantages as well as drawbacks and is to be used in specific situations. Companies can select the most effective option for their particular requirements by understanding the difference between deep and machine learning.

FAQ Section

  1. What is Machine Learning?

    Machine learning is a subset of artificial intelligence that involves teaching computers to learn from data without being explicitly programmed. It involves training a model on a set of data to make predictions or decisions based on new data.

  2. What is Deep Learning?

    Deep learning is a subset of machine learning that uses neural networks with multiple layers to learn hierarchical representations of data. It involves training models with large amounts of data to perform tasks such as image recognition, natural language processing, and speech recognition.

  3. What are some common algorithms used in machine learning?

    Some common algorithms used in machine learning include linear regression, logistic regression, decision trees, random forests, k-nearest neighbors, support vector machines, and neural networks.

  4. What is overfitting?

    Overfitting occurs when a model is trained too well on the training data, to the point where it memorizes the training data instead of learning the underlying patterns. This can result in poor performance on new, unseen data.

  5. What is underfitting?

    Underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both the training and test data.

Other Links:

» The Use of Machine Learning in Software development

» What is Artificial Intelligence?

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