Deep Learning and Machine Learning: Differences
A machine learning certification can help you develop the key skills that you need to get a job in the field. Machine Learning is a rapidly growing field and is creating various new job opportunities. If you want to make a career in machine learning, you must be aware of what ML is all about, how it works, and what are different types of machine learning. Along with this, understanding the difference between machine learning and deep learning is also important.
You might be using the terms machine learning and deep learning interchangeably. However, the two are not the same. Deep learning is a branch of machine learning, but there are several differences between the two techniques. You can see plenty of examples of Machine learning and deep learning models in your day-to-day lives. But identifying which one uses deep learning becomes challenging until you understand both fields thoroughly.
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What is Machine Learning?
Machine Learning is a subset of CS (Computer Science) and AI (Artificial Intelligence). It focuses on providing machines with the ability to learn on their own without any programming requirements. Machine Learning makes use of past data to train models and find or predict accurate outcomes. In general programming, you give an input to get an output. But in machine learning, you give both input and output to the machines and let them learn from it. ML algorithms can be of three types: classification, clustering, and regression models. Different algorithms are suitable for different types of data. Some most useful Ml algorithms include Naive Bayes, Decision Tress, Random Forest, K-means algorithm, etc. Talking about the real-world use cases of ML, fraud detection, recommendation engines, email spam filtering, etc, are some most common applications of machine learning.How Does Machine Learning Work
You might wonder how machines can learn on their own. It’s just like a child learning how to identify different colors, objects, etc. Suppose you want to teach kids about different colors. You will pick a black-colored object and tell them that it is black color. The next time you pick the same object, you will expect the kid to identify its color and they will most probably do it. This is exactly how machines learn. Firstly, you need to provide the machines with data, which is called training data. This data includes inputs and corresponding outputs. Some inputs, known as test data, are fed into the model to test its accuracy. After this, the ML model is used to predict the outcomes for new inputs. The accuracy of these predictive models depends on the quality of training data.Types of Machine Learning
Machine Learning can be majorly classified into three types based on the methods of learning and type of training data: Supervised Machine Learning: In Supervised Machine Learning, the machines are trained with labeled datasets. The input is mapped with the corresponding output and then these models are used to predict outcomes for new inputs. For example, you can feed the images of dogs and cats along with their respective features, so that machines can identify dogs and cats based on the given list of features. Unsupervised Machine Learning: In unsupervised machine learning, you train the machines using unlabeled datasets. The ML models are provided with unsorted datasets and they have to categorize these datasets by looking at the similarities, differences, and patterns. For example, supermarkets can use unsupervised ML algorithms to group their customers based on their interests, spending behavior, etc. Reinforcement Learning: Reinforcement learning works on the concept of feedback. In this, the ML models are rewarded for every desired action and punished for undesired behavior. As a result, the models tend to improve by themselves in order to receive maximum rewards. Also Read: What’s the relationship between big data and machine learning?What is Deep Learning?
Deep Learning can be defined as a subdomain of machine learning. The field uses artificial neural networks, which resemble the neurons network in the human brain. With the help of these artificial networks, deep learning models are able to make decisions just like humans. As humans learn from their past mistakes, these models also learn from errors and improve accordingly. In short, deep learning is a field that focuses on mimicking the way the human brain works. Some most common applications of deep learning include image recognition, natural language processing, self-driving cars, language translation, etc.How Does Deep Learning Work?
Deep learning works in an entirely different way from ML models. In this, you feed the input to the first layer of neural networks. The first layer is known as the input layer and the last layer is called the output layer. All the layers between the first and the last layer are named hidden layers. There can be any number of hidden layers in these neural networks. Each of these layers has a group of neurons, where the actual data processing happens. The input data is transferred from one layer to another via weighted channels. These channels are responsible for transforming this data within the hidden layers. Then the input is multiplied by the channel’s weight value and the result is passed to the next layer. Once a layer receives the information from the previous layer, it gets activated and starts processing the data. This process continues and the weights of every channel are adjusted in order to deliver the best outcomes.Different Types of Deep Learning Algorithms
There are different types of deep learning algorithms to deal with almost every kind of data. Here are some most popular deep-learning models: Convolution Neural Networks (CNNs): majorly used for object detection and image processing. Recurrent Neural Networks (RNNs): majorly used for handwriting recognition, time series analysis, image captioning, and machine translation. Autoencoders: majorly used for image processing, popularity prediction, and pharmaceutical discovery. Generative Adversarial Networks (GANs): majorly used to upscale low-resolution pictures and improve astronomical images.Deep Learning vs Machine Learning
Now, you understand what machine learning and deep learning are all about, how the two fields work, and what their major applications are. Let us help you understand the key differences between machine learning and deep learning via the following table:| Machine Learning | Deep Learning |
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Learn Machine Learning & Deep Learning at Edvancer
If you are an aspiring ML engineer, the best way to start your career journey is to find a course that prepares you for a machine learning job. Edvancer is a great platform that focuses on career-oriented education. You can find the following three machine learning certification courses at Edvancer:- IBM Professional Certificate in AI & Machine Learning
- IIT Kanpur Advanced Certification in AI and Machine Learning
- Certified Machine Learning with Python Expert
FAQs
1. Is deep learning a type of machine learning? Ans. Deep learning is a subdomain of machine learning that uses neural networks resembling the neurons of the human brain. The aim of deep learning is to help machines in making decisions just like humans do. 2. Can we learn deep learning without machine learning? Ans. Yes, it is possible to directly learn deep learning without machine learning. However, understanding deep learning algorithms might be challenging until you are aware of the fundamentals of AI and ML. It is advisable to learn ML before you dive into deep learning. 3. What is an example of deep learning? Ans. Some common examples of deep learning applications include NLP (Natural Language Processing), image recognition, speech recognition, language translation, self-driving cars, etc. 4. What is an example of machine learning? Ans. A few popular examples of machine learning applications are recommendation systems, spam email filtering, fraud detection, etc.Share this on
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