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Cover image for Machine Learning Vs. Deep Learning: How Do They Differ?
Aryan K Khanna
Aryan K Khanna

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Machine Learning Vs. Deep Learning: How Do They Differ?

Getting in-depth insight into the latest development in the AI sector might seem overwhelming; it does not matter whether you have a technical background or not. But if you want to know the basics of this futuristic technology, the discussion could be narrowed down to two concepts: machine learning and deep learning. Both of which come with certain differences. So, what are they? Read out to explore and check out other interesting details like:

  • Definition of machine learning and deep learning & their working methods
  • The Prime Difference Between Machine Learning and Deep Learning
  • Conclusion

Definition of Machine Learning and Deep Learning & Their Working Methods

Machine Learning

Machine learning (ML), a branch of artificial intelligence (AI) focuses on developing algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed. It enables machines to analyze and interpret complex patterns and data and adapt their behavior based on experience.

This technology has varied applications in domains like finance, healthcare, and robotics. ML is revolutionizing all these industries and driving advancements in data-driven decision-making.

With its ability to handle large datasets and uncover valuable insights, ML is transforming the way we solve complex problems and unlocking new possibilities for innovation.

How Does ML Work?

Going through a practical example of ML will provide you with a better understanding of its working method. Take a movie streaming app as an example. Such apps employ machine learning algorithms to identify the choice of a specific viewer and suggest content with similar tastes.

When discussing theoretically, ML makes use of algorithms and helps computers to learn from data, and makes predictions or decisions without explicit programming.

It works by following the key steps like Data collection, Data preprocessing, feature selection/engineering, model training, and evaluation.

During the training phase, the ML model analyzes the input data, identifies patterns, and adjusts its internal parameters for performance improvement. The model is then tested on unseen data to evaluate its accuracy. Through this iterative process, the model learns from the data and becomes capable of making predictions or taking required actions on new, unseen data.

Deep Learning

It represents a notable advancement in the machine learning sector and gained significant attention due to groundbreaking outcomes. These algorithms work by mimicking human reasoning by analyzing data using logical structures, employing both supervised and unsupervised learning approaches.

This transformative technology has helped in unlocking unparalleled potential for understanding and processing complex data and fuelling unexplored possibilities across several industries.

Working Method of Deep Learning

DL utilizes artificial neural networks (ANNs) with multiple layers for data processing and analysis.

Its working process starts with input data, passed through the layers of interconnected nodes, known as neurons. Their role is to perform a weighted computation on the available input data and to apply an activation function to offer the output.

Through an iterative process, known as training, the network adjusts its internal weights based on the discrepancy between predicted outputs and the expected outputs.

This adjustment is achieved with the help of algorithms optimization. By iteratively optimizing the weights, deep learning models become able to extract relevant features from the data automatically, enabling them to make accurate predictions or classifications.

By using this approach, deep learning excels in tasks like image recognition, natural language processing, and pattern recognition.

The Prime Difference Between Machine Learning and Deep Learning

Architecture and Complexity

ML: Machine learning algorithms come with simpler architectures, such as decision trees, random forests, or support vector machines. These algorithms rely on feature engineering and domain knowledge for extracting features from data.
DL: It utilizes artificial neural networks (ANNs) with multiple hidden layers. These deep neural networks can automatically learn hierarchical representations of data, eliminating the requirement of feature engineering.


ML: Machine learning models work more effectively on small to medium-sized datasets, effectively capturing patterns and making accurate predictions within their scope.
DL: Deep learning models work better while handling massive datasets. The intricate architecture of deep neural networks enables them to efficiently process and learn from the abundant data, resulting in improved performance.

The depth and complexity of deep learning models allow them to handle the intricacies of large-scale datasets, making them well-suited for tasks such as image recognition, natural language processing, and other high-dimensional data analysis.

Feature Extraction

ML: Features extraction in ML needs human intervention for relevant feature identification. The success of ML models heavily relies on the quality and relevance of these handcrafted features.
DL: Deep learning models can learn automatically and can extract the required features from raw data. This ability of DL makes this model more flexible and suitable to perform different tasks.

Scopes for Implementation

ML: Machine learning has a range of applications in several sectors like text classification, recommendation systems, fraud detection, and sentiment analysis.
DL: Deep learning champions in areas where complex patterns and representations are crucial, such as image and speech recognition, natural language processing, autonomous driving, and medical diagnostics.

Requirements for Data
ML: Machine learning models need a moderate amount of labeled data for training. Insufficient training data may result in ML models underperforming or being prone to overfitting.
DL: Unlike ML, Deep learning models require large amounts of labeled data for training. The vast number of parameters in deep neural networks enables them to effectively leverage big data. DL models often exhibit better performance with more data without creating concerns about over-fitting.

ML: Machine learning models come with great interpretability, allowing humans to understand and reason about the decision-making process. Models like decision trees or linear regression provide clear insights into feature importance and the underlying decision rules.
DL: Deep learning models, particularly with complex architectures like deep convolutional neural networks (CNNs), are often considered black boxes. They can deliver superior performance but lack interpretability.


So, after completing the reading now you got an exposure to these two futuristic technologies properly. Keep exploring them more and start your journey to get the deepest insight into the limitless and dynamic world of AI!

Answering Some Common FAQs on Machine Learning and Deep Learning

DL or ML- Which of These Two is Better?
As said before, ML is ideal for small to medium-sized datasets, while deep learning excels with large datasets. So, none of them can be called better concretely. It depends on your specific use case, offering reliability for smaller datasets or the ability to extract intricate patterns from extensive data collections.

Can Both of These Learn from Unstructured Data?
Yes, both these technologies are capable to learn from unstructured data. ML algorithms can analyze and extract valuable insights from images, text, or other unstructured formats. DL, with its deep neural networks, excels in understanding complex unstructured data like images and natural language.

Does DL Come with a Better Accuracy Rate?
DL can achieve higher accuracy rates compared to traditional ML models, especially when dealing with large and complex datasets. The ability of deep neural networks to learn hierarchical representations allows them to uncover intricate patterns, leading to improved accuracy in various tasks.

What are the Ethical Considerations in ML and DL?
Ethical considerations include the facts of privacy protection, fairness, transparency, and accountability. It is important to address biases, ensure user privacy, and develop transparent decision-making processes in ML and DL models.

Are Large Datasets Always Necessary for ML and DL Models?
The dataset size depends on the complexity of the problem and the model architecture. As said before, ML models can work effectively with smaller datasets, while DL models, with their vast network architectures, often require larger datasets to achieve optimal performance.

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