Difference Between Machine Learning and Deep Learning

Artificial intelligence represents a vast and complex domain featuring a broad ecosystem of terminology, expressions, and ideas that can seem daunting when discussed by technologists and other specialists. In popular culture, the term artificial intelligence is frequently employed as a broad term to refer to any kind of intelligent machine.
 

Machine Learning and Deep Learning represent the two primary ideas of Data Science and are subdivisions of Artificial Intelligence. Many individuals believe that machine learning, deep learning, and artificial intelligence are merely interchangeable buzzwords. However, in reality, all these terms vary but are interconnected.
 

In this discussion, we will explore What is machine learning, and how machine learning differs from deep learning. However, before exploring Deep Learning vs. Machine Learning, let’s begin with a short overview of machine learning and deep learning.

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Machine Learning vs. Deep Learning: Key Differences

Here is a breakdown of Deep Learning vs. Machine Learning under various parameters.

Definition and Concept

Machine learning and deep learning are both types of artificial intelligence, with deep learning as a subset of machine learning and Machine learning as a subset of AI. Consequently, deep learning falls under the umbrella of machine learning, yet it differs from conventional machine learning techniques. 

Deep Learning offers distinct benefits compared to other Machine Learning methods, establishing Deep Learning as the leading algorithmic technology of today's time. 

Machine Learning employs algorithms that enhance their performance as the volume of data increases. Conversely, deep learning relies on layers, whereas machine learning relies on data inputs to learn independently. 

Learning Process 

While both ML and DL enable machines to learn from data, the methods of learning or training employed by the two technologies differ. 

ML consists of four primary training techniques: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Additional training techniques consist of transfer learning and self-supervised learning. 

In comparison, deep learning algorithms employ various types of more intricate training techniques. These consist of convolutional neural networks, recurrent neural networks, generative adversarial networks, and autoencoders. 

Data Requirement

Deep Learning needs significantly more data compared to a conventional Machine Learning algorithm to perform effectively. Machine Learning operates with thousands of data points, while deep learning typically utilizes millions. Owing to its intricate multi-layer design, a deep learning system requires a substantial dataset to minimize variations and produce high-quality interpretations. 

Computing Power

Due to their intricate nature and the need for extensive datasets, deep learning models necessitate greater storage and computational resources compared to ML models. Although ML data and models can operate on a solitary instance or a server cluster, a deep learning model typically necessitates high-performance clusters and considerable infrastructure. 

The infrastructure needs for deep learning solutions can lead to costs that are considerably greater than those for ML, which is the major difference between ml and dl. On-premises infrastructure might not be feasible or economical for implementing deep learning solutions. You can utilize scalable infrastructure and completely managed deep learning services to manage expenses. 

Human Intervention

Deep Learning algorithms need significantly less human input. Recollect the Tesla case? If image recognition for the STOP sign used a more conventional machine learning approach, a software engineer would select features and a classifier to categorize images, verify if the output meets expectations, and modify the algorithm if it does not. As deep learning algorithms, the feature is automatically extracted, allowing the algorithm to learn from its mistakes. 

Applications

The choice between using ML or deep learning relies on the kind of data you must analyze. ML recognizes patterns from structured data, including classification and recommendation systems. For example, a business can utilize ML to forecast when a client is likely to cancel their subscription based on historical customer churn information. 

Conversely, deep learning approaches are better for unstructured data, as they require a significant degree of abstraction to uncover features. Deep learning tasks encompass image classification and natural language processing, which require the recognition of intricate relationships among data elements. For instance, a deep learning system can examine social media mentions to assess user sentiment. 

Also read:- Career in Data Science After Mechanical Engineering

How Do Machine Learning and Deep Learning Work?

Both machine learning and deep learning undergo certain processes for an outcome. Let's crack them below;

1. Machine Learning Process: 

• Training Data: A labeled dataset is supplied to the algorithm, in which each data point has a defined result. 
• Algorithm Choice: A suitable machine learning algorithm (e.g., decision tree, linear regression, support vector machine) is selected based on the issue at hand. 
• Model Development: The algorithm examines the training data, recognizing patterns and connections between input features and the desired output. 
• Assessment: The trained model is evaluated on fresh data to determine its accuracy and effectiveness. 

2. Deep Learning Process: 

• Artificial Neural Networks: Deep learning models are created with several layers of linked nodes (neurons) that replicate the framework of the human brain. 
• Forward Propagation: Input data is fed into the network layers, where each layer executes calculations on the data, progressively identifying more intricate features. 
• Backpropagation: The model evaluates its predictions against the real target values and modifies the weights of the neurons to reduce the error, enabling the network to learn and enhance. 

Which One Should You Choose?

Confused about which one you should go for? In this scenario, both DL and ML have unique features you can choose from those norms. Let's analyze them below;

1. Deep Learning

• Deep Learning surpasses other methods when the volume of data is substantial. However, when dealing with limited data size, conventional Machine Learning algorithms are more suitable. 
• Deep Learning methods require advanced infrastructure to train within a reasonable timeframe. 
• In cases where there is insufficient domain knowledge for feature introspection, Deep Learning methods excel compared to others since you need to focus less on feature engineering. 
• Deep Learning excels particularly in tackling intricate issues like image classification, natural language processing, and speech recognition. 

2. Machine Learning 

Employ Machine Learning in the subsequent scenarios: 

• You cannot define the rules: Numerous human activities like determining if an email is spam or not spam cannot be effectively addressed with a deterministic, rule-based approach. Many factors may affect the response. When regulations rely on numerous factors and many of these regulations intersect or require precise adjustments, it quickly becomes challenging for a person to accurately program the rules. ML can be utilized to successfully address this issue. 
• You cannot scale: You may be capable of manually identifying a few hundred emails and determining if they are spam or not. Nonetheless, this task turns monotonous when dealing with millions of emails. ML solutions are proficient in addressing large-scale challenges. 

3. Career paths: 

1. ML Engineer: Create, build, and implement machine learning models for practical use cases, which involve data preparation, model training, hyperparameter adjustment, and model evaluation. 

Average Salary: Rs 11.5 Lakhs - Rs 13.5 Lakhs 

2. Data Scientist: Acquire a solid grasp of statistical modeling, data preprocessing, feature extraction, and model assessment while utilizing machine learning techniques to address issues. 

Average Salary: Rs 13.5 Lakhs 

3. AI Researcher: Explore advanced machine learning algorithms, creating novel methods and extending the limits of the discipline. 

Average Salary: Rs 8 Lakhs 

4. Deep Learning Specialist: Concentrate on sophisticated neural network structures such as CNNs, RNNs, and transformers to address intricate issues in fields like computer vision and natural language processing. 

Average Salary: Rs 9 Lakhs 

Conclusion

In conclusion, both ML and DL play important roles in the progress of AI. To conclude with Deep Learning vs Machine Learning, Machine Learning allows robots to learn from their experiences and enhance their performance as time progresses. Deep Learning is applied to address particular issues that are challenging to tackle with conventional Machine Learning methods, like image and speech recognition.
 

As technology advances swiftly, delving into AI-related courses can greatly improve your career opportunities by providing you with advanced skills in data analysis, machine learning, and problem-solving, rendering you highly sought after in diverse fields, leading to innovative positions and securing your career's future. You can explore more online degree courses and make an informed choice.

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