Machine learning and deep learning are both sub-disciplines of artificial intelligence. They’re very similar in certain ways because they have the same purpose: automation.
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There remains a huge debate about these two disciplines because people want to know which one is better. However, they are both useful in different cases. If you’re trying to understand the differences between ML and DL, then this guide is for you.
What Is Machine Learning?
Machine learning is a sub-discipline of artificial intelligence that deals with a machine’s ability to learn without human intervention. Due to machine learning, computers can identify patterns, errors, and trends in data.
Even though we use machine learning in our daily lives, many people are confused about what machine learning is because the term sounds futuristic.
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Machine learning (ML) can be used to predict the outcome of a situation or replicate a human’s actions. There are many ML algorithms, such as linear regression, SVM, decision trees, logistic regression, and Naive Bayes classifiers. There are three kinds of ML learning: reinforcement, supervised, and unsupervised learning:
- Supervised learning. This is an ML approach in which data is input into a computer to generate a specific expected output. For example, machines can be taught how to differentiate between coins because each one has a particular weight. The device will then be able to deduce the type of coin based on its weight. This is called labeled data.
- Unsupervised learning. Unsupervised learning does not use any labeled data. This means that the machine must independently identify patterns and trends in a dataset.
- Reinforcement learning. RL is like teaching a child to read. If the child doesn’t read a sentence correctly, they will be told they made a mistake, and how they can improve. Then, they can identify and correctly pronounce a word the next time they see it. The same thing happens with ML reinforcement learning.
Machine Learning Salaries and Job Outlook
There are many types of jobs within machine learning, but the most common is a machine learning engineer.
A machine learning engineer is one of the highest-paid careers in the tech industry. This is because ML engineers are in high demand, but there are not many experienced professionals to fill the roles. The average machine learning engineer earns around $112,350 per year, according to PayScale.
If you’d like to pursue a career as a machine learning engineer, you don’t necessarily need a bachelor’s degree. There are also many machine learning bootcamps that can prepare you for employment.
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Among the other possible jobs in machine learning are machine learning research scientist, machine learning scientist, and senior machine learning engineer.
What Is Deep Learning?
Deep learning (DL) is an advanced form of artificial intelligence. While machine learning is a sub-discipline of AI, deep learning is a sub-discipline of machine learning. It is referred to as a type of ML inspired by the anatomy of the human brain. DL is used for image classification, recognizing speech, and translations.
Have you ever wondered how Google can translate almost every single page on the Internet? Or how it classifies images based on who is in the photo? Deep learning algorithms are responsible for these technological advancements.
DL is structured as an artificial neural network that mimics human intelligence. It is considered more efficient than machine learning.
Deep Learning Salaries and Job Outlook
Like machine learning engineers, deep learning engineers also usually receive a high salary because their skills are in high demand. Any job related to AI has become much more valuable as the field has continuously expanded. According to PayScale, the average salary of a deep learning engineer is $114,772.
Deep Learning vs Machine Learning: The Most Important Differences and Similarities
A debate has emerged in the AI industry over whether deep learning or machine learning is more useful. Many professionals believe that DL is more accurate than ML, while others prefer ML for digital products. Regardless of which side you’re on, both disciplines have important applications in the modern era.
Many of the things we do each day, such as typing on our smartphones or making a biometric bank payment are based on either ML or DL. Even though deep learning is a subset of machine learning, the two disciplines are very different.
Independency
Machine learning usually requires engineers to input data so that the machine can identify and differentiate between items. With deep learning, this part of the process is not necessary. Instead, the algorithm can identify features on its own by using deep neural networks. This makes deep learning more independent than machine learning.
Here is another example. In machine learning, if a machine makes a mistake while identifying data, an engineer has to address the problem and make an adjustment. With deep learning, this step can be skipped. The deep learning model will identify the inaccuracy on its own and act accordingly.
Learning vs Reasoning
The term machine learning describes a device’s ability to learn, while deep learning refers to a machine’s ability to make decisions based on data. Because of this, translations done via ML are not as accurate as those conducted using DL. Machine learning does not take into account the context of a sentence, while deep learning does.
The Structure
The structure of machine learning is very simple when compared to the structure of deep learning. ML may use a basic decision tree or linear regression, while DL involves a complex structure known as a multilayer artificial neural network.
Data Requirements
Both disciplines use data to perform tasks such as image identification or making predictions. The more data the machine has access to, the more accurate its predictions will be.
Their Purpose Is the Same
Both disciplines have the same goal of identifying patterns without human intervention. However, one of them is more independent than the other.
Machine Learning Vs Deep Learning: Required Skills and Duties
DL and ML engineers are both AI professionals. However, the two jobs require different skills and have different duties. If you’d like to become a machine learning or deep learning engineer, you should have the skills listed below.
Machine Learning Skills
- Computer science fundamentals. Just like any software developer, you must have a good understanding of computer science basics such as programming, computer architecture, and data structure.
- Probability and statistics. This discipline is heavily related to data science, so you should also have a good understanding of probability and statistics.
- Data modeling and evaluation. Data modeling skills are essential in machine learning. It is the process of defining and analyzing a dataset to come up with actionable insights.
Machine Learning Job Duties
- Developing machine learning applications. As a machine learning engineer, you must be able to develop ML applications to meet your project requirements. To do this, you need to perform problem framing, data collection, and feature engineering.
- Running machine learning tests and experiments. Machine learning requires you to conduct experiments using a variety of methods and techniques. This is a daily task of any ML engineer.
- Selecting appropriate data representation methods. Choosing how data should be represented is an important step in the statistics and prediction process. This is one of the fundamental duties of machine learning engineers.
Deep Learning Skills
- Natural language processing. NLP is one of the most essential skills in AI. If you want to become a deep learning engineer, you must understand the similarities between computers and the human brain.
- Robotic process automation. As an engineer, you must be able to configure machines to perform human-like activities.
- Data science skills. Remember that data science is the foundation of all AI-related disciplines. As a deep learning engineer, you will need to understand the fundamentals of data science. You also might want to look further into the differences between data science, machine learning, and AI.
Deep Learning Job Duties
- Develop effective deep learning systems. Deep learning engineers must know how to code so that they can develop efficient AI-driven systems.
- Test DL modules. Just like machine learning engineers, DL engineers must run experiments and tests to make sure they are implementing the right strategies.
- Configure Robotics Process Automation. This involves creating software that allows a robot to emulate human actions and carry out repetitive tasks.
Should You Become a Deep Learning Engineer or Machine Learning Engineer?
Both deep learning and machine learning skills are in high demand in the tech sector. Becoming an engineer in either sector will be a rewarding and well-paid job. Which field you will choose should depend on your existing skills and preferences.
Advantages of Becoming a Machine Learning Engineer
Machine learning engineering is a discipline that helps you solve real-life problems and develop practical solutions. You will have countless job opportunities if you plan to go into machine learning. Besides, this job is high-paying. If you already know you’d like to pursue a career in tech, this could be a good path to take.
Advantages of Becoming a Deep Learning Engineer
Starting in deep learning could lead to a very interesting professional journey. Remember that many companies are looking to hire deep learning engineers for new projects, so your skills will be very valuable. Deep learning engineers are also well paid, so take this into consideration when choosing your career.
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