{"id":6614,"date":"2018-01-29T08:04:52","date_gmt":"2018-01-29T02:34:52","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=6614"},"modified":"2025-07-28T15:41:46","modified_gmt":"2025-07-28T10:11:46","slug":"deep-learning-vs-machine-learning","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/deep-learning-vs-machine-learning\/","title":{"rendered":"Deep Learning vs Machine Learning &#8211; Demystified in Simple Words"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:2043,&quot;href&quot;:&quot;https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/Deep_learning&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251012001035\\\/https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/Deep_learning&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-10 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22:08:23&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-03 07:22:11&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-06 23:32:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-10 05:33:41&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-13 07:57:37&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-16 16:21:43&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-19 20:45:17&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-23 09:12:31&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-28 03:54:18&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-31 05:44:34&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-04 08:01:00&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-07 11:01:35&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-10 13:12:50&quot;,&quot;http_code&quot;:404}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-10 13:12:50&quot;,&quot;http_code&quot;:404},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<div>\n<div class=\"\">\n<p>Deep Learning and Machine Learning are the two most trending technologies in the world today. These technologies are often used interchangeably.\u00a0While Deep Learning is the subset of machine learning, many people get confused between these two terminologies. So, for clearing this confusion today, we came up with our new article &#8211; Deep Learning vs Machine learning. This article consists of the feature-wise difference between both. Also, we will discuss applications, future trends, and where<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/deep-learning-vs-machine-learning.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-55007\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/deep-learning-vs-machine-learning.jpg\" alt=\"deep learning vs machine learning\" width=\"803\" height=\"421\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/deep-learning-vs-machine-learning.jpg 803w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/deep-learning-vs-machine-learning-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/deep-learning-vs-machine-learning-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/deep-learning-vs-machine-learning-768x403.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/deep-learning-vs-machine-learning-520x273.jpg 520w\" sizes=\"auto, (max-width: 803px) 100vw, 803px\" \/><\/a><\/p>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Introduction to Deep Learning and Machine Learning<\/h3>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">1. What is Machine Learning?<\/h4>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-tutorial\/\"><strong>Machine Learning is the scientific study<\/strong> <\/a>of statistical models and algorithms that computer systems use to perform a task without explicit instructions. Machine Learning is a comprehensive field that involves various functionalities of machine learning operations like clustering, classification and development of predictive models.<\/p>\n<p>Basically, Machine Learning allows computers to learn without an explicit need for programming.<\/p>\n<p>In general programming scenarios, you have to provide instructions to the computer for it to provide you with the output. However, with the help of machine learning algorithms, you can train your computer to provide you with the output with the need to give instructions. A machine learning algorithm is able to do so with the help of data. Using the data fed to the system, a machine learning algorithm is trained to provide output to the users. There are three main types of <strong><a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-algorithms\/\">machine learning algorithms<\/a><\/strong> &#8211;<\/p>\n<h5>1.1 Supervised Learning<\/h5>\n<p>In a supervised learning algorithm, the input data is labeled such that the data is organized. The computer is able to follow the examples of input-output pairs and train the model to fit the data with good accuracy. Some of the supervised learning algorithms are &#8211;<\/p>\n<ul>\n<li>Linear &amp; Multivariate Regression<\/li>\n<li><strong><a href=\"https:\/\/data-flair.training\/blogs\/logistic-regression-in-r\/\">Logistic Regression<\/a><\/strong><\/li>\n<li>Naive Bayes<\/li>\n<li>Decision Trees<\/li>\n<li>K-nearest neighbour<\/li>\n<li>Linear Discriminant Analysis<\/li>\n<li>Artificial Neural Networks<\/li>\n<\/ul>\n<h5>1.2 Unsupervised Learning<\/h5>\n<p>In Unsupervised Learning, the data is not labeled or categorized. In Unsupervised learning, the data is able to organize itself after it follows a certain pattern in the way the data is distributed. Unsupervised learning algorithms are complex and are currently under research. Some of the unsupervised learning algorithms are &#8211;<\/p>\n<ul>\n<li><strong><a href=\"https:\/\/data-flair.training\/blogs\/r-clustering\/\">Clustering Analysis<\/a><\/strong><\/li>\n<li>Anomaly Detection<\/li>\n<li>Hierarchical Clustering<\/li>\n<li>Principal Component Analysis<\/li>\n<\/ul>\n<h5>1.3. Reinforcement Machine Learning Algorithms<\/h5>\n<p>We use these algorithms to choose an action. Also, we can see that it is based on each data point. Moreover, after some time the algorithm changes its strategy to learn better. Also, achieve the best reward.<\/p>\n<p>Machine Learning is used in various industries that require future prediction, identification of patterns and autonomous decision making. It is widely used in healthcare, finance, banking, manufacturing and transportation sectors.<\/p>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">2. What is Deep Learning?<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">\n<p><strong>Deep Learning is a recent field<\/strong> that occupies the much broader field of Machine Learning. Deep Learning is most famous for its neural networks such as<em> Recurrent Neural Networks, Convolutional Neural Networks, and Deep Belief Networks<\/em>. While other machine learning algorithms employ statistical analysis techniques for pattern recognition, Deep learning is modeled after the neurons of the human brain.<\/p>\n<p>They are modeled after the structure and functioning of the human brain. In order to understand deep learning, we have to understand how the nervous system in the human body works. As we all know that our nervous system is built up of neurons. These neurons are able to grasp information that is transmitted to our body. These neurons have the ability to learn information over time. This principle of \u2018learning\u2019 is also utilized by<a href=\"https:\/\/data-flair.training\/blogs\/artificial-neural-network-model\/\"> <strong>artificial neural networks<\/strong><\/a>.<\/p>\n<p><em>Any Deep neural network will consist of three types of layers:<\/em><\/p>\n<\/div>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The Input Layer<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The Hidden Layer<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The Output Layer<\/li>\n<\/ul>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Deep-neural-networks.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-55677\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Deep-neural-networks.jpg\" alt=\"Deep neural networks\" width=\"366\" height=\"420\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Deep-neural-networks.jpg 366w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Deep-neural-networks-131x150.jpg 131w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Deep-neural-networks-261x300.jpg 261w\" sizes=\"auto, (max-width: 366px) 100vw, 366px\" \/><\/a>In the above example, the layers are present in the form of<strong> input layer<\/strong>, that takes the input data. The<strong> hidden layer<\/strong>, which performs various computation on the input data and the <strong>output layer<\/strong>, which in the above visualization is binary. It is to be noted that a neural network can have multiple hidden layers.<\/p>\n<p>Another aspect in which deep learning models perform notably is feature extraction from raw data which is usually done by developers by feature extraction from raw data. This is especially useful in such tasks as image and speech where it is difficult to select the features to use by hand. Therefore, deep learning has contributed enormously to the evolution of diverse sectors, for instance, natural language processing and self-driving cars.<\/p>\n<p>These neural networks are used to predict the output as well as perform classification on the data. The standard notion is that the neural network learns the pattern of data, then performs predictions that fall in the same line as the pre-specified pattern.<\/p>\n<\/div>\n<\/div>\n<div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Deep Learning vs Machine Learning<\/h3>\n<p>We use a machine algorithm to parse data, learn from that data. And make informed decisions based on what it has learned.\u00a0Basically, deep learning is used in layers to create an artificial \u201cneural network\u201d. That can learn and make intelligent decisions on its own.<\/p>\n<p>Let&#8217;s understand the comparison of deep learning vs machine learning through their features &#8211;<\/p>\n<\/div>\n<\/div>\n<div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">1. Data dependencies<\/h4>\n<p>Performance is the main key difference between both algorithms. Although, when the data is small, deep learning algorithms don\u2019t perform well. This is the only reason <strong>Deep Learning algorithms<\/strong> need a large amount of data to understand it perfectly.<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Why-deep-learning-01.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-55055\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Why-deep-learning-01.jpg\" alt=\"Why deep learning is best\" width=\"802\" height=\"420\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Why-deep-learning-01.jpg 802w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Why-deep-learning-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Why-deep-learning-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Why-deep-learning-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Why-deep-learning-01-520x272.jpg 520w\" sizes=\"auto, (max-width: 802px) 100vw, 802px\" \/><\/a><\/p>\n<p>But, we can see the use of algorithms with their handcrafted rules prevail in this scenario. Above image summarizes this fact.<\/p>\n<\/div>\n<\/div>\n<div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">2. Hardware dependencies<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Generally, deep learning depends on high-end machines. While traditional learning depends on low-end machines. Thus, <strong><a href=\"https:\/\/en.wikipedia.org\/wiki\/Deep_learning\">deep learning<\/a><\/strong> <span class=\"complexword\">requirement<\/span> includes GPUs. That is integral part of it\u2019s working. Also, they do a large amount of matrix multiplication operations.<\/div>\n<\/div>\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">3. Feature engineering<\/h4>\n<p>It\u2019s a general process. In this, domain knowledge is put into the creation of feature extractors. Also, to reduce the complexity of the data. Further, make patterns more visible to learn algorithm working. Although, it\u2019s very difficult to process. Hence, it\u2019s time consuming and expertise.<\/p>\n<h4>4. Problem Solving approach<\/h4>\n<\/div>\n<div>\n<div class=\"\">\n<p>Generally, we use traditional algorithm to solve problems. Although, it needs to break a problem into different parts. Further, solve them individually. Moreover, to get a result, combine them all.<\/p>\n<p><strong>For Example &#8211;\u00a0<\/strong><\/p>\n<p>You have a task of multiple object detection. Although, in this task we have to identify what is the object and where is it present in the image. Further, in a machine learning approach, we have to divide the problem into two steps:<\/p>\n<ul>\n<li>Object detection<\/li>\n<li>Object recognition<\/li>\n<\/ul>\n<p>First, we use grabcut algorithm to skim through the image and find all the possible objects. Then of all the recognized objects, you would then use object recognition algorithm like SVM with HOG to recognize relevant objects.<\/p>\n<\/div>\n<\/div>\n<div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">5. Execution time<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Usually, deep learning takes more time as compared to machine learning to train. The main reason behind its long time is that so many parameters in deep learning algorithm. Whereas machine learning takes much less time to train, ranging from a few seconds to a few hours.<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">6. Interpretability<\/h4>\n<p>We have interpretability as a factor for comparison of both learning techniques. Although, deep learning is still thought 10 times before its use in industry.<\/p>\n<p>The last crucial issue that should be discussed is related to the possibility of interpretation of models. Again, traditional machine learning models give clearer answers where they performed the elaboration of a specific case, while deep learning models with numerous nested layers are not so transparent. This can be a limitation particularly in disciplines in which it is necessary to understand the reason behind decisions.<\/p>\n<p><strong>You must check the master comparison of <a href=\"https:\/\/data-flair.training\/blogs\/data-science-vs-artificial-intelligence-vs-machine-learning-vs-deep-learning\/\">AI vs Data Science vs ML vs Deep Learning<\/a><\/strong><\/p>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Where is Machine Learning and Deep Learning Being Applied?<\/h4>\n<\/div>\n<div class=\"\">\n<h5 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">1. Computer Vision<\/h5>\n<p>We use this both for different applications like vehicle number plate identification and facial recognition.<\/p>\n<\/div>\n<h5>2. Information Retrieval<\/h5>\n<p>We use ML and DL for applications like search engines, both text search, and image search.<\/p>\n<\/div>\n<h5>3. Marketing<\/h5>\n<p>We use this learning technique in automated email marketing, and in target identification.<\/p>\n<h5>4. Medical Diagnosis<\/h5>\n<p>It has very wide usage in the medical field also. Applications like cancer identification, anomaly detection<\/p>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><a href=\"https:\/\/data-flair.training\/blogs\/ai-natural-language-processing\/\"><strong>Natural Language Processing<\/strong><\/a><\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">For applications like sentiment analysis, photo tagging, online Advertising, etc<\/li>\n<\/ul>\n<\/div>\n<h3>Applications of Deep Learning &amp; Machine Learning<\/h3>\n<p>Following are some of the real-life applications of Deep Learning and Machine Learning &#8211;<\/p>\n<ul>\n<li>Machine Learning technologies are being widely used for medical imaging. For finding tumors and other malignant in the human body.<\/li>\n<li>In the field of Marketing, Machine Learning based time-series forecasting are being used for predicting sales.<\/li>\n<li>Deep Learning is playing a major role in the development of industrial robotics.<br \/>\nIn the self-driving car industry, machine learning algorithms are used for navigating the vehicle to its destination.<\/li>\n<li>Natural Language Processing is being used by the industries to analyze customer reviews and gain insights about their sentiments.<\/li>\n<li>E-commerce industries are using Deep Learning based recommendation systems to provide insights to the customers based on their purchasing patterns.<\/li>\n<\/ul>\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Future Trends<\/h3>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Nowadays machine learning and data science are in trend. In companies, demand for both is <span class=\"adverb\">rapidly<\/span> increasing. Also, their demand particularly for some companies. i.e. company who wants to survive to inculcate Machine Learning in their business. Also, it&#8217;s necessary to know basic terminologies.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Deep learning <span class=\"passivevoice\">is discovered<\/span> and prove the best technique with state-of-the-art performances. Thus, deep learning is surprising us and will continue to do so <span class=\"complexword\">in the near future<\/span>.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Recently, researchers are continuous in Machine Learning and Deep Learning. Nowadays, research in ML and DL is making their place in both industry and academia.<\/li>\n<\/ul>\n<p><strong>Learn more about the <a href=\"https:\/\/data-flair.training\/blogs\/future-of-machine-learning\/\">future of machine learning\u00a0<\/a><\/strong><\/p>\n<\/div>\n<div class=\"\">\n<h3>Summary<\/h3>\n<p>In this article, we studied various concepts behind Deep Learning and Machine Learning. We understood how they function, their principle analogies and their usages. Also, we discussed the feature-wise comparison of deep learning vs machine learning.<\/p>\n<p>Machine learning is used for simple problems like predicting house prices, spam detection, or sales forecasting. Deep learning is used in complex tasks like self-driving cars, facial recognition, and natural language processing. In short, deep learning is machine learning\u2014but deeper, smarter, and more data-hungry. Both have their own place depending on the size, complexity, and type of problem.<\/p>\n<p>If you have any questions about deep learning and machine learning, comment below. We will definitely get back to you.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Deep Learning and Machine Learning are the two most trending technologies in the world today. These technologies are often used interchangeably.\u00a0While Deep Learning is the subset of machine learning, many people get confused between&#46;&#46;&#46;<\/p>\n","protected":false},"author":5,"featured_media":55007,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[2728,3673,15697,16466],"class_list":["post-6614","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-comparision-between-deep-learning-and-machine-learning","tag-deep-learning-vs-machine-learning","tag-what-is-deep-learning","tag-what-is-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Deep Learning vs Machine Learning - Demystified in Simple Words - DataFlair<\/title>\n<meta name=\"description\" content=\"Deep Learning vs Machine Learning - A feature-wise comparison. 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