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"AI is a computer algorithm which exhibits intelligence through decision making. ML is an AI algorithm which allows system to learn from data. DL is a ML algorithm that uses deep(more than one layer) neural networks to analyze data and provide output accordingly. Search Trees and much complex math is involved in AI."

Artificial Intelligence is a field of study that develops machines/systems to exhibits intelligence. You can think it as a goal to develop intelligent machines. Now we can achieve this in multiple ways. One of the way is using machine learning algorithms which are statistical methods and learns from data to predict an outcome or classify an input. Deep learning again is a sub class of machine learning algorithms which uses artificial neural network to achieve the goal of AI.

Artificial intelligence is a vast discipline that emulates the cognitive abilities of the human brain, enabling sophisticated decision-making. Machine learning and deep learning are discrete subfields included within the broader realm of artificial intelligence. Machine learning primarily addresses classification and regression problems, whereas deep learning tackles similar tasks but utilizes more intricate models, such as employing hundreds of neurons to learn from data points. This is in contrast to other machine learning methods, such as artificial neural networks, which deploy fewer neurons and layers of neurons. To summarize, artificial intelligence comprises several approaches such as machine learning, deep learning, natural language processing, and reinforcement learning while machine learning focus on specific task as mentioned above.

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are closely related fields, but they are not the same. Each represents a different aspect or level of a broader spectrum in developing and applying intelligent systems.

Artificial Intelligence (AI)

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1. Definition: AI is a broad field of computer science concerned with building intelligent machines capable of performing tasks that typically require human intelligence. The overarching concept encompasses everything from robotic process automation to actual robotics.

2. Scope: AI includes reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects. It covers various applications, from simple calculators to self-steering technology to something that might radically change the future.

3. Techniques: AI techniques include rule-based systems, decision trees, and machine learning, among others.

Machine Learning (ML)

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1. Definition: ML is a subset of AI. It's the science of getting computers to

act without being explicitly programmed. In ML, systems learn from data, identify patterns, and make decisions with minimal human intervention.

2. Scope: ML focuses on developing computer programs that can access and use data to learn for themselves. It's more about designing algorithms that can learn from and make predictions on data.

3. Techniques: ML techniques include linear regression, support vector machines, decision trees, random forests, neural networks, and clustering.

Deep Learning (DL)

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1. Definition: DL is a subset of ML. It imitates the workings of the human brain in processing data and creating patterns for use in decision-making. It's a field that involves the use of large neural networks.

2. Scope: DL mainly deals with high-dimensional data such as images, sound, and text. It's primarily known for its feature detection and unstructured data extraction effectiveness.

3. Techniques: DL techniques are based on deep neural networks, which are neural networks with multiple layers. These layers can hierarchically learn the data's features.

In summary:

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- AI is the broadest concept for machines designed to act intelligently like humans.

- ML is a subset of AI involving methods and techniques that enable machines to improve at tasks with experience.

- DL is a subset of ML that uses multi-layered neural networks to simulate human decision-making processes.

Hi,

I hope you good

Artificial intelligent (AI) refer to the development of computer systems capable to performing tasks that typically require human intelligence.

Machine Learning (ML) is a subset of AI that involves the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data.

Deep Learning (DL) is a specialized subset of ML that uses neural networks with many layers (hence "deep") to learn representations of data.

Artificial intelligence (AI)

Broad field aiming to create intelligent machines.

Includes various techniques like rule-based systems and statistical methods

Machine Learning (ML)

Subset of AI focused on algorithms that learn patterns from data.

Types:supervised,unsupervised,reinforcement Learning.

Deep Learning (DL):

Subset of ML using neural networks with multiple layers.

Particularly powerful for complex tasks like image and speech recognition