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Machine Learning Vs Deep Learning: Difference Between Machine Learning and Deep Learning

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24th Aug, 2020
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Machine Learning Vs Deep Learning: Difference Between Machine Learning and Deep Learning

Machine learning and Deep learning both are the buzzwords in the tech industry. Machine learning and deep learning both are the subdivision of artificial intelligence technology. If we further breakdown, deep learning is a subdivision of machine learning technology.

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If you are familiar with the basics of machine learning and deep learning, it is excellent news!

However, if you are new to the AI field, then you must be confused. What is the difference between machine learning and deep learning?

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There is nothing to worry about. This article will explain the differences in easy to understand language.

What is Machine Learning?

Machine learning is a branch of technology that studies computer algorithms. These algorithms allow the system to learn from data or improve by itself through experience. Machine learning algorithms make predictions or decisions without being explicitly programmed.

To make it simple, let me remind you of a few AI applications that you used. Do you remember playing chess with a computer? Yes, that was the early days of AI. These chess games were the result of hard-coded algorithms that are designed by a programmer. A computer programmer thought of a series of smart moves with the best outcomes and written codes for these chess games.

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Machine learning is far ahead of the early days of AI algorithms. Machine learning algorithms are not designed with hard-coded rules to solve the problem. These algorithms learn by themselves by feeding them real-world data. It means as time passes, machine learning algorithms become smart and make a prediction of their own.

Let’s take an example and understand how these algorithms learn on their own. Feed a collection of images of rabbit and mouse to ML algorithm. Now you want to identify the pictures of rabbit and mouse separately with the use of the ML algorithm. You must feed structured data to the ML algorithm to work. Now label the specific features of the rabbit and mouse in images and present it to ML algorithm. ML algorithms will learn the distinct characteristics of these two animals from this labelled data. It continues to identify millions of images of rabbits and mice based on features it learned from labels.

Read more: Deep Learning vs Neural Networks: Difference Between Deep Learning and Neural Networks

What is Deep Learning?

Deep learning is a branch of machine learning that is made of virtual neurons in the successive layer. Deep learning is extremely flexible, and it is inspired by human brain function. The work of each neuron is to analyze the input coming into it and decide whether to transfer the output to the next neurons or not. Every neuron in a layer is connected. The neuron network can solve a large number of problems, just like the human brain. 

To understand how deep learning works, Let us take the same example of Image identification of rabbit and mouse. To solve this problem, deep learning networks will take a different approach. The advantage is, it does not need structured or labelled data to identify the animal.

When we feed rabbit and mouse images to deep learning neural networks, this input will pass through a different layer of neurons. Each layer of neurons in the hierarchy will define a specific feature of the image and move it to the next level. Now can you see the similarity between deep learning networks and the human brain? The human brain also solves the problem by passing it to a different hierarchy of concepts and queries and finding a solution.

Once data is processed through a different layer of neuron network, it will create a specific identifier to classify both animals.

Key Differences between Machine Learning & Deep Learning

These are just basic examples to explain how machine learning and deep learning works. Now let us sum-up key differences:

  • Machine Learning requires structured data and learning from labelled features. In comparison, Deep Learning does not require structured or labelled data and processes the data within the artificial neuron network.
  • Machine Learning algorithms are designed in such a way that they learn to do things with experience. Whenever the desired output is not received, it requires human intervention to retrain the algorithm. In comparison, deep learning neural networks learn from their errors and do not require human intervention. However, if the input is not of good quality, even deep learning can give undesired output since they produce output through a layered neuron network.

As we have seen in both cases, input data is essential. The quality of input data decides the quality of output.

Let us also have a look at usages of machine learning and deep learning:

Usage of Machine Learning

  •  In an organization that has some structured data, machine learning can be useful. They can use this data easily to train machine learning algorithms.
  • The intelligent application of machine learning solutions can help in the automation of various business processes.
  •  It can also be used to develop chatbots.

Usage of Deep Learning

  • When an organization is dealing with a massive amount of unstructured data, deep learning is a better option.
  •  In the case of complex problems, deep learning provides better solutions.

Demand for Machine Learning and Deep Learning in Data Science and AI

In a company, a considerable amount of data is generated daily. A lot of crucial information goes unnoticed due to the ample amount of data. Now companies have very well understood the power of data analysis. In-depth data processing can generate various insights that will serve many business purposes.

Machine learning, Deep Learning, Data Science, and AI are becoming an integral part of every growing business. These technologies have already entered our lives as well in the form of modern-day assistants. If you take insight, whether it is Netflix or Amazon, they are using these technologies for their business growth.

When you browse a specific product on Amazon, unknowingly, you are generating data. These data are analyzed by a Data Scientist to understand your interest. Have you ever noticed the pattern of Ads when you are watching YouTube or Netflix? These Ads are of similar products from your browsing history. How does this happen? It is nothing but data science doing its work.

Now understand the connection between data science and machine learning.

Data Science is used to do analysis and processing of data. The primary purpose is to extract meaningful outcomes for business purposes. Data Science involves not only data processing but also data extraction, data cleansing, data analysis, data visualization, and data generation of actionable insight. There are tons of data that go unnoticed in business. 

A Data Scientist is a person who is responsible for extracting meaningful insight from these data. By analyzing the data pattern, data scientists shed light on production outcomes, customer behaviour, and other business purposes. Data Science is essential for companies to beat market competition and enhance customer satisfaction.

So, the question arises, what is the role of machine learning in data science?

In simple words, Machine Learning is a part of Data Science. As we discussed, data is generated in a massive amount in companies. It becomes a tedious task for a Data Scientist to work on it. So here comes the role of machine learning. Machine Learning uses statistics and algorithms to process and analyze data. All these data processing and analysis are done without human intervention. You can also say machine learning is an ability given to the system to process, analyze, and provide insight to outcomes on its own.

Machine Learning and Deep Learning are some of the functionalities of data science. However, these technologies are used for a distinct purpose in artificial intelligence.

Machine learning, when combined with AI, becomes a powerful combination. Now companies are looking for digital automation vigorously. One of the ways to do business process automation is with the use of Robotic Process Automation. RPA uses both AI and machine learning to automate business processes. Now robots are replacing humans for mundane and repetitive work. It helps companies with better resource utilization.

As you can see, ML, AI, and Data Science play a crucial role in digital transformation. The fact is that every company is dealing with massive data, repetitive work, and demanding customers. The whole world is moving toward digital transformation. In this scenario, technology like machine learning, deep learning, AI, and data science are a rage in demand.

Skills Required

Any professional who is interested in the latest technology and upskilling can learn machine learning and deep learning. To pursue a career in this field, the professional must be skilled in followings:

  • It requires a thorough understanding of statistics, algorithms, an expert in drawing probability form data, and making predictive models and the ability to solve confusion matrices.
  • A very crucial skill required for machine learning is data modelling. A professional must have an in-depth understanding of how data modelling works, accuracy measures for given errors, and working evaluation strategy.
  • Along with the skill mentioned above, professionals must keep themselves up to date with the latest technologies, development tools, and algorithms.

How to master the required skills?

upGrad is a one-stop solution for all your technology needs. After understanding the market demand and individual upskilling needs, upGrad has designed various courses. upGrad offers multiple courses related to AI, Data Science, Machine Learning, and Deep Learning. Let us have a look at their courses:

All these courses are designed, keeping industry demand in mind. These courses are outlined as per working professional needs. Throughout the course, industry experts will provide their guidance to students. For a better learning experience, dedicated mentors will be provided to students.

Whoever wants to take their career to the next level can pursue these courses. The minimum eligibility criteria are any bachelor’s degree and no coding background required. The best part is after completion; of course, you will be awarded prestigious recognition from IIIT-B. 

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Machine learning, Deep learning, AI, and Data Science are in high demand. Businesses are moving towards digital transformation at a fast pace. The first step towards change is automation and in-depth insight into organization data.

As per The Hindu, “Machine will Rule Workplace by 2025”. The World Economic Forum says: “More than 54% of India’s employees in 12 sectors need reskilling by 2022”.

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The industrial revolution is at its peak. Every company wants to automate their process. To be market leaders, it is crucial to have an in-depth understanding of operational requirements and faster processes to save time and customer satisfaction.

It is imperative to understand that technologies are moving at a fast pace, and automation is at the rage. Robots will take over all the repetitive, mundane, and massive data tasks. In such a scenario, the human workforce will be utilized for better work. Now upskilling is mandatory to stay in the competition.

Machine learning and deep learning is the backbone of the latest technologies. The trends also show that Machine Learning and Deep Learning will play a vital role in business process automation. So, mastering the skill which is on high demand will bring limitless opportunities for you.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1When is the use of deep learning not preferred?

Deep learning does not perform well in the case of complex hierarchical structures due to the large quantity of complex data involved. One of the key reasons why deep learning might produce unsatisfactory results in the case of a few enterprises or organizations is the lack of a sufficiently large corpus of properly labelled, high-quality data. Deep learning is also not recommended if you do not have a large budget because it is highly expensive and requires GPUs and a large number of machines.

2When is the use of machine learning not preferred?

A vast quantity of data is required by machine learning systems. Another issue lies with the quality of the given data. The model's accuracy can be greatly reduced or dangerous predictions might be made due to poor data quality. If a rule-based system can perform well for less complex issues, then it is preferable to avoid using a machine learning system and opt for a rule-based system.

3Which one can provide me with a better job-machine learning or deep learning?

Deep learning is a subset of machine learning. Both machine learning and deep learning are interconnected, despite having a few dissimilarities. Knowledge of both of these helps you land a good-paying job. However, what may be a better job for you may not be a good one for another person. Thus, you should really focus where your interest lies to grab the job of your dreams.

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