Artificial Intelligence is making waves in the tech world. It’s the next exciting frontier, and it’s already transforming industries from healthcare to finance. But what is artificial intelligence? How does it work? To answer those questions, let’s dive into the basic concepts of AI: machine learning and deep learning.

Artificial Intelligence

Artificial intelligence (AI) is a field of computer science that involves giving computers the ability to reason, learn, and solve complex problems. AI has been around for over 60 years but has recently seen a surge in interest because of recent advances in computing power and big data processing. The goal of AI is to develop systems that allow computers or machines to perform tasks normally requiring human intelligence for example visual perception, speech recognition, or decision making under uncertain conditions. The concept behind machine learning is that software can be trained on large amounts of data (i.e., millions of pieces of information), without being explicitly programmed with all the knowledge required to make decisions when given new inputs. Deep learning is a subset of machine learning that uses deep neural networks which are loosely inspired by biological processes like neurons in the brain or DNA transcription/translation reactions within cells.

Machine Learning

Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed.

Machine learning is a subset of artificial intelligence (AI), which itself is a subfield of computer science. So what’s AI? It’s the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. The study of how machines can do these things is known as machine learning.

Data science is an interdisciplinary field that uses scientific techniques to analyze data and extract knowledge from it — it’s where statistics meets programming meets computer science! Machine learning falls under this umbrella: it’s a subset of data science because it deals specifically with computational methods for making predictions based on data.

Deep Learning

In the field of artificial intelligence, deep learning is considered to be a subfield of machine learning. In computer science, it’s considered a subfield of artificial intelligence (AI).

In statistics, it’s considered one of many subfields under the overarching umbrella of statistical modeling. And in mathematics? Well, that’s where things get interesting.

Many mathematicians think about deep learning as being part of their domain — but others disagree because they think that deep learning is too broad and isn’t really “mathematical.” Of course, there are some who would argue that all fields are ultimately mathematical in nature.

Machine Learning vs. Deep Learning

Many people find it difficult to distinguish between Machine Learning and Deep Learning. While they are both subsets of AI, there are some important differences worth noting.

Machine learning is a field that focuses on the development of algorithms that can learn from data. More specifically, machine learning algorithms make inferences from data without being explicitly programmed to do so. This is different from traditional software programming where the programmer would have to enter rules or dependencies for every single possibility (for example: if x then y). Instead, machine learning uses statistics and probability theory to give computers this ability by finding patterns in large amounts of data — for example in images or text documents — and making predictions about new data based on those associations.

Deep Learning is a particular branch of machine learning that involves deep neural networks (DNNs) which consist of multiple layers stacked together in order to recognize patterns in complex datasets such as images or sound files; these systems can derive context from one layer to another resulting in an outcome more accurate than traditional methods using smaller datasets consisting only.

Conclusion

AI is an exciting field and we have barely scratched the surface of its potential to improve our lives. This article should give you an overview of AI, ML and DL, which are all tools that can be used to achieve this goal. There are many more topics in this realm but hopefully, this introduction has given you an overview of what makes up these fields.

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Resources ⚡️

Link 1: guru99.com/artificial-intelligence-tutorial..

Link 2: mygreatlearning.com/blog/what-is-artificial..

Link 3: levity.ai/blog/difference-machine-learning-..