Machine learning vs deep learning: Key differences

Machine learning is often confused with artificial intelligence. Machine learning, deep learning, and artificial intelligence interrelated, but quite different from each other. It is essential to learn about machine learning and deep learning differences. 

Artificial intelligence is a branch of science focusing on making computers think and behave like humans. Computers excel at computing and executing a task with 100% efficiency; however, doing a simple task that a human does every day might be out of the syllabus for a computer.  

Training computers to behave like humans and perform a simple task that we do in our daily life is a complicated task. 

Let’s consider a scenario to understand this. Imagine a restaurant where robots take orders and serve a crowded bar with drinks. It might sound like a cake piece to you; however, the robot might have to do the complex computation to serve the right orders to the correct customers. 

Machine learning and deep learning are the methods to train computers to execute a simple task precisely and quickly. Machine learning trains a computer to perform a task through explicit programming.

If you are wondering how deep learning is related to machine learning, deep learning is a subset of machine learning that trains the computer using an artificial neural network. 

Deep down, it’s all interrelated. 

What is Machine Learning?

Machine learning can be defined as a subset of artificial intelligence that helps to teach and train computers to predict accurate outcomes without explicit programming to perform the task. Computers are given structured data, and machine learning algorithms train the computer to become better at evaluating data and processing the data with time. 

Are you wondering what structured data is? Imagine structured data that data inputs that can be easily classified and put in rows and columns. It would be easy to understand this with an example. Imagine you create a “‘Food” excel sheet and have two-row entries, namely fruit and meat. Computers can effortlessly work with structured data, and after programming, you can feed new data inputs to the computer. With the help of a machine learning program, the computer sorts and acts on the data without any human intervention. At some point, the computer would learn that fruit is a type of food, and it might not need your help to label the data anymore. 

In simple words, machine learning trains the computer by feeding examples until the computer learns to differentiate. Data science is related to machine learning and provides the data it requires to process, learn more about it here. 

Talking about machine learning vs deep learning, deep learning is more efficient and bare accurate results than machine learning.

Different types of machine learning methods 

There are different methods of machine learning by which you can train the computer. The entire machine learning process stays the same; however, the type of data you feed to the computer changes. Let’s learn about the different types of ML methods and what data a data scientist provides to the different ML methods. If data science intrigues you, click here to learn more about it. 

1. Supervised Learning 

As the name suggests, the supervised learning method requires structured data to train the computer. Data scientists feed the algorithm well-labeled data and define the variables they want the computer to assess for correlation. 

2. Unsupervised Learning 

In unsupervised learning, the computers are fed with unstructured data. Unstructured data does not follow a conventional data model, meaning it would be tedious to fit unstructured data into excel sheets. There are no training sets or prior knowledge given to the computer; the computer learns through discovery and observation as it analyzes the data. 

3. Semi-supervised learning 

Semi-supervised learning requires semi-structured data. The data scientists feed the computer well-labeled and defined data; however, the computer is free to explore the data and develop an understanding by analyzing it on its own.   

4. Reinforcement Learning

Reinforcement learning is about taking a suitable action to maximize the reward or output in a particular situation. In supervised learning, the training data comes with an answer key, however, in reinforcement learning, the answer is unavailable, so the learning agent decides to perform the task. The data scientists give positive and negative cues as it finds a way to complete the task. 

 If you are interested in exploring machine learning methods, click here. 

What is Deep Learning?

Talking about how deep learning is related to machine learning, deep learning can be classified as a sophisticated approach to machine learning that trains the computer to make decisions like humans and process the data as a human brain does. Complex, multi-layered, deep neural networks have different nodes through which the data transfers, and the machine extracts key features as it processes the data. 

Different types of deep learning algorithms

 

There are three popular types of deep learning algorithms: 

1. Convolutional neural network (CNN)

CNN deep learning algorithms extract features from images and video sets to complete a specific task. It can be used for image classification, face authentication, and semantic segmentation. 

2. Recurrent neural network (RNN)

Like CNN deals with images, RNN deals with memory. In RNN, the computer stores the past data points and decisions in its memory and uses the past information while reviewing the current data. It is used in natural language processing tasks, like machine translation, word embedding, and natural language modeling.

3. Multi-layer perceptron (MLP) 

Multi-layer perceptron is a type of feed-forward neural network with densely connected layers that transform the input into the desired output. Deep neural networks in machine learning can help in pattern classification, recognition, prediction, and approximation. 

Machine Learning vs. Deep Learning

Here are some points that explain machine learning and deep learning differences:

No.	Machine Learning	Deep Learning
1.	Machine learning uses statistical methods to train machines and improves learning with experience.	Deep learning uses neural networks like the human brain to help machines make decisions and extract features from the data.
2.	Machine learning is a subset and helps in developing Artificial Intelligence.	However, Deep Learning is a subset of Machine Learning and one of the techniques used to train machines.
3.	Machine Learning focuses on increasing the accuracy to achieve the results rather than the success ratio.	Deep Learning produces results with high accuracy when trained with massive data.
4.	Its efficiency is less as compared to deep learning as it cannot work with large datasets.	It is highly efficient to process large data sets.

Future of machine learning and deep learning 

We live in a high-tech world where technology is helping all the different sectors, like business, healthcare, and others, to automate their processes and upgrade their industries. Here is how machine learning and deep learning can make a difference and make the future looks promising and bright: 

1. Optimizing operations in document management. If you are interested in reading how AI can change the marketing industry, click here.

2. Enhanced healthcare as machine learning programs can look into patients’ health history to devise personalized treatment plans. 

3. Reducing and detecting fraud will help the banking and finance institution to identify fake documents, filter phishing emails, predict fraudulent transactions, and prevent identity thefts. 

4. Provide customers with personalized services on entertainment and social media platforms like face swap filters and Netflix’s recommendation features.

5. Deep neural networks in machine learning will help develop models that mimic human behavior. 

Conclusion

We live in a period where technology is evolving, and we are interested in finding new ways to simplify our daily lives. The invention of computers changed our lives, and the evolution of the computer will change our lifestyle.  

Machine Learning and Deep learning algorithms are used productively as new technologies unfold. A recent study shows that by 2025, more than 97 million people will work in AI space. Undoubtedly, learning about machine learning and deep learning today will be worthwhile and bear sweet fruits in the future. 

If you are interested in learning what machine learning and deep learning are, you can opt for online courses available on the Online Manipal website. We offer online graduate, post-graduate, and certification programs specifically designed for you. You can learn at your pace, and our experienced faculty will assist you so you can have a brighter future. 

 Key takeaways : 

• Machine Learning is a branch of science that tackles writing algorithms to train a computer to make decisions with increased accuracy. 

• There are four types of machine learning algorithms that differ from the type of data we feed to the machines. They are – supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

• Deep Learning is a type of machine learning technique that works on artificial neural networks. Artificial neural networks consist of densely connected nodes that process the data and extract essential features to analyze the data. 

• There are three popular types of deep learning algorithms – CNN, RNN, and MLP.