Artificial Intelligence, Machine Learning, and Deep Learning continue to impact our lives in new and innovative ways every passing day. These technologies have made our routine life far easier than it used to be a decade ago.
You might have noticed that many routine tasks that took hours before, can now be accomplished within a matter of few clicks. These technologies have affected almost every industry across every sector including BFSI (Banking, Finance Services, and Investments), Software Products, Information Technology, Healthcare and Pharmaceuticals, Manufacturing, Media and entertainment, Logistics, Transport, and many more.
This way, there is a massive demand for professional experts in any of these technologies, AI, ML, or Deep Learning. Can you believe that the online job portal Indeed.com has ranked Machine Learning Engineer as #1 among the Best Jobs in the US? The growth rate for this job role is a whopping 344%. Whoa!!
These figures are fueling the number of professionals looking for an excellent AI and ML course so that they can enter into this ever-growing career domain.
The terms Artificial Intelligence, Machine Learning, and Deep Learning are often used interchangeably. But Machine Learning is a subset of Artificial Intelligence and Deep Learning is a subset of Machine Learning. So, simply put, ML works behind the scenes of Artificial Intelligence; and Deep Learning works behind the scenes of Machine Learning.
Let us have a look at these trending technologies and the major differences between the two.
What is Machine Learning?
Machine Learning is a subset of Artificial Intelligence that uses techniques including Deep Learning to enable machines to improve on their own on the basis of experience. The following steps make the learning process:
- Input the data into an algorithm
- Utilise this data in order to train your model
- Testing and deploying the model
- Check the performance of the model by assigning it to carry out an automated predictive tasks
With Machine Learning, you need not reprogram your model as it learns and improves on its own, without the need for human intervention.
What is Deep Learning?
You might have the habit of watching videos on YouTube or movies on Netflix. You might have noticed that it recommends the songs, videos, or movies that match your interest. How come these apps know about interests? Is there someone who is keeping an eye on what you usually watch? Yes! Someone is there.
Deep Learning!!
Being a subset of machine learning, Deep Learning uses artificial neural networks that are designed to mimic the ways humans think or learn. The input is given to the deep learning systems in the form of large data sets on which these systems work and learn and come up with the desired output.
The ANNs (Artificial Neural Networks) work on the series of True/False or binary questions based on the input data, include complex mathematical computations, and provide the result.
Machine Learning Vs Deep Learning: Key Differences
Although both the technologies are subsets of Artificial Intelligence and both work on learning processes based on data input to them, there are some key differences between Machine Learning and Deep Learning.
Human Intervention
With Machine Learning systems, you need to identify and explicitly code the applied features based on the input data types; on the other hand, the Deep Learning system attempts to learn the features without human intervention.
As in the case of an image recognition system, a model identifies the objects and then the more prominent parts of the figures, and then the different representations. All this is carried out by neural networks that learn on their own without human intervention, and come up with accurate results with experience.
Approach
While in machine learning, algorithms parse data in small chunks and the results are then integrated to give the desired solution; deep learning considers the problem as a whole. For example, take the case of image recognition.
A machine learning algorithm first performs object detection and then its recognition. On the other hand, a deep learning program will take the entire image as input, performs both object recognition and location recognition, and comes up with the desired result at once.
Hardware
As we know that deep learning involves artificial neural networks to process the data, it requires more complex hardware than machine learning. Also, the hardware requirement for deep learning depends on the amount of data to be fed (which is generally enormous) and the complexity of computations.
The most important hardware used in Deep learning is a GPU or Graphical Processing Unit. While on the other hand, a machine learning algorithm can be executed on low-end machines with low computational power.
Time
Machine Learning takes a few minutes or a few hours as the level of complexity of computations is low. When you consider Deep Learning, the amount of data to be fed as input is generally huge and the parameters or computational tasks are also huge in number, deep learning systems usually take a lot of time to train.
Applications
Since both the terms are used interchangeably, we think that deep learning and machine learning algorithms have different applications.
Typical machine learning algorithms include email spam identification, predictive programs like a weather forecast or stock forecast, and algorithms that make treatment plans based on the medical history of patients.
One of the most common use cases of deep learning is in virtual assistants like Apple’s Siri, Amazon Alexa, or Google Assistant. Other applications of deep learning include chatbots, video recommendation platforms like Netflix, YouTube, Amazon, and more, robotics, image captioning, and more.
Conclusion
To conclude, it is clear that machine learning and deep learning technologies have numerous applications in our lives and both of them are continuously evolving. To make a career in any of these domains, you can go with an online training course and get related certifications added to your resume. You can work as a Machine Learning Engineer, Data Scientist, Deep Learning Engineer, and other related job roles.
Enroll Yourself Now!!
