Between Machine learning and Artificial Intelligence

The knowledge of artificial intelligence [AI] being used by "intelligent" computers to mimic human thought and carry out independent activities has almost become a norm after gathering enough momentum to become a new practice in modern work culture. 

This process by which a computer system becomes intelligent is called machine learning. Furthermore, using a neural network, which is a collection of algorithms based on the human brain, is one method for teaching a computer to imitate human reasoning. 

As we proceed, you must bear in mind that this article has been put together by The Watchtower, a web design agency in Dubai and a leading name in the business of web design and development in Dubai. 

Machine learning is a method of data analysis that automates the building of analytical models. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. 

Is it better to learn machine language [ML] before artificial intelligence [AI]? 

It is not necessarily better to learn machine learning before artificial intelligence, but it can be helpful to have a solid understanding of machine learning concepts and techniques before diving into the broader field of AI. 

Machine learning is a subset of AI and focuses specifically on the development of algorithms and statistical models that enable systems to learn from data and make predictions or decisions without explicit instructions. 

AI, on the other hand, encompasses a wider range of technologies and techniques, including machine learning, but also includes other areas such as natural language processing, computer vision, and robotics. 

It's not always a strict requirement to learn machine learning before AI, as they are related but not the same. Having a background in either one of them will be beneficial in understanding the other. It's also important to note that many ML techniques can be used in AI and vice versa, so understanding both can be useful. 

It is possible to learn the concepts of machine learning without coding, but it will be difficult to apply those concepts and build models without a basic understanding of programming. Most machine learning tasks are performed using programming languages such as Python, R, and Java. 

There are some tools and platforms that provide a user-friendly interface to apply machine learning models without coding, such as RapidMiner, KNIME, and Big Panda. These platforms allow you to drag and drop different modules and visualize the results, but they still require some knowledge of data preprocessing and machine learning concepts. 

Another way to learn machine learning without coding is by taking online courses that provide a mix of theory and applications; some of them even have interactive tutorials and exercises to practice the concepts. But to become proficient in ML and apply it to real-world problems, it would be beneficial to learn the coding part as well. 

Machine learning (ML) and artificial intelligence (AI) are related but distinct fields. 

The main difference between ML and AI is that ML is a specific subset of AI that focuses on the development of algorithms and statistical models that enable systems to learn from data and make predictions or decisions without explicit instructions. 

AI, on the other hand, encompasses a wider range of technologies and techniques, including machine learning, but also includes other areas such as natural language processing, computer vision, and robotics. 

Another difference is that machine learning is mainly focused on pattern recognition and prediction, while AI is focused on creating intelligent agents that can perform tasks that normally require human intelligence. 

In summary, machine learning is a subset of AI, and it deals with the development of algorithms and models that enable computers to learn from data and make predictions or decisions, while AI is a broader field that encompasses machine learning but also includes other areas such as natural language processing, computer vision, and robotics, and its main focus is to create intelligent agents that can perform tasks that normally require human intelligence.