Fatema Alalyan Machine learning (ML) and deep learning (DL) are both subsets of artificial intelligence (AI) that focus on enabling machines to learn from data and make decisions. However, they differ significantly in their approaches, architectures, and applications.

Machine Learning involves algorithms that allow computers to learn patterns from data and make predictions or decisions without being explicitly programmed. Traditional ML techniques, such as linear regression, decision trees, and support vector machines, rely on structured data and often require manual feature extraction. In ML, engineers identify relevant features from data, preprocess it, and then train models. These models are effective for smaller datasets and simpler problems, such as spam detection, fraud detection, and price prediction.

Deep Learning, on the other hand, is a specialized branch of machine learning that uses artificial neural networks designed to mimic the structure and function of the human brain. It is particularly effective at handling unstructured data like images, audio, and text. Deep learning models automatically learn features through multiple layers of neurons, removing the need for manual feature engineering. These models require large datasets and high computational power, but they excel in tasks such as image recognition, speech processing, and natural language understanding.

In summary, while machine learning is suitable for simpler tasks with structured data, deep learning is ideal for complex problems involving large datasets and unstructured data, leveraging neural networks to extract patterns automatically.

Dear Fatema Alalyan

Machine learning and deep learning are both branches of artificial intelligence, but they differ significantly in their methods and applications. Machine learning is a broader concept that includes algorithms that enable computers to learn from and make predictions based on data without the need for explicit programming. Deep learning, on the other hand, is a specialized form of machine learning that uses multi-layered neural networks (hence the name “deep”) to process complex data such as images, audio, and text.

Fatema Alalyan

Machine learning is like training algorithms by showing them a bunch of examples and guiding them through making decisions. It's like giving a student a set of rules and lots of practice problems to learn from.

While, Deep learning, on the other hand, is like teaching a computer or training algorithm by letting it figure things out on its own with a huge amount of information. It's similar to giving a student access to a massive library and letting them find patterns and solutions by themselves through exploration and connections.

Machine learning relies more on manual feature engineering and simpler algorithms, Deep learning leverages complex neural networks with many layers to automatically extract features from large datasets, making it suitable for tasks like image and speech recognition.

Hussain Nizam

Thank You

In simple words, deep learning is a subfield of machine learning that structures algorithms in layers to create an “artificial neural network” that can autonomously learn and make intelligent decisions.

thanks