Artificial Intelligence is not a thing, though that’s how movies portray it.<\/p>\n\n\n\n
It’s more of an umbrella term that brings together several subfields of computer science. This field is divided into multiple parts, algorithms, theories, and applications.<\/p>\n\n\n\n
Each has different goals and methods to pursue them. Some are achieving their goals better than others doing it in the same timeline or even close to it.<\/p>\n\n\n\n
Machine learning is one of the subfields of Artificial Intelligence<\/a>. It refers to the process of getting a computer to learn from data without being explicitly programmed.<\/p>\n\n\n\n The term machine learning was coined in 1959<\/a>, but its history<\/a> goes back to the pre-code era (around the mid 19th century) with the discovery of Bayes’ Theorem<\/a>. It became popular around the 90s and should not be confused with other terms like ‘Artificial Neural Network’ or ‘Deep Learning.’<\/p>\n\n\n\n Related: Why a Personal Curriculum is Important for Data Science Students<\/a><\/em><\/strong><\/p>\n\n\n\n There are siblings to machine learning under the parenthood of artificial intelligence<\/a>. Some of the siblings are natural language processing, cognitive computing, robotics, and computer vision. These fields were built around different concepts than machine learning.<\/p>\n\n\n\n Natural language processing<\/a> is teaching computers to understand and generate human languages.<\/p>\n\n\n\n It combines the rule-based modeling of human language with statistical, machine learning, and deep learning algorithms. Today, NLP is applied to many tasks, such as machine translation, text summarization, and dialogue systems.<\/p>\n\n\n\n Cognitive computing<\/a> is the process of making a computer system that can think like humans.<\/p>\n\n\n\n A breakthrough in this subfield is the discovery of neural networks. Neural networks<\/a> are a way of simulating the workings of the human brain.<\/p>\n\n\n\n Related: How to Evaluate if Deep Learning Is Right For You?<\/em><\/strong><\/a><\/p>\n\n\n\n Robotics<\/a> is a subfield of AI that deals with the design, construction, and operation of robots. Robotics is perhaps the most mature subfield of AI and has seen significant commercial deployment.<\/p>\n\n\n\n Computer Vision<\/a> means the ability of computers to interpret and understand digital images. It is mainly used in tasks such as facial recognition, object recognition, and scene understanding.<\/p>\n\n\n\n It serves as a subfield of AI on its own, but it’s also being used in other fields.<\/p>\n\n\n\n The first known use was back in the 50s by Alan Turing, who introduced it in his Computing Machinery and Intelligence<\/a> paper.<\/p>\n\n\n\n After that, researchers have been building programs on machine learning which are now critical to the success of many AI applications.<\/p>\n\n\n\n Machine learning is more widespread and has more research done on it. It’s also been commercially successful in specific areas such as predictive analytics, fraud detection, and search engines.<\/p>\n\n\n\n Machine learning is not a standalone subfield, but it is a critical component to other successful subfields.<\/p>\n\n\n\n One example is in computer vision, where machine learning is used for tasks such as object recognition and scene understanding.<\/p>\n\n\n\n In natural language processing, machine learning can be used for tasks such as text classification and sentiment analysis.<\/p>\n\n\n\n Where to start learning machine learning? For example, you can think of machine learning as a subset of predictive analytics that deals with pattern recognition and decision-making.<\/p>\n\n\n\n So if you have some knowledge of statistics, it will be easier to understand the concepts behind machine learning.<\/p>\n\n\n\n It’s also helpful to have programming skills since a lot of machine learning is done through coding.<\/p>\n\n\n\n But wait, even if your statistical knowledge and programming skills aren’t very good, you can still become a machine learning engineer.<\/p>\n\n\n\n Related: How to Become a Terrific Data Scientist (+Engineer) Without Coding<\/em><\/strong><\/a><\/p>\n\n\n\n Visual analytics tools such as KNIME and RapidMiner make it easy for you to learn and use machine learning without having to code.<\/p>\n\n\n\n These tools provide a graphical interface where you can drag-and-drop algorithms and connectors to create data pipelines.<\/p>\n\n\n\n You don’t need any coding skills to do this, and you can get started in minutes.<\/p>\n\n\n\n Thanks for reading, friend! Say Hi to me on LinkedIn<\/a>, Twitter<\/a>, and Medium<\/a>.<\/p>\n\n\n\nOther fields of AI that are not machine learning.<\/h2>\n\n\n\n
Natural Language Processing (NLP)<\/h3>\n\n\n\n
Cognitive Computing<\/h3>\n\n\n\n
Cognitive computing is used in fields such as image and speech recognition,<\/h3>\n\n\n\n
Robotics<\/h3>\n\n\n\n
Computer Vision<\/h3>\n\n\n\n
Machine learning still plays a central role in Artificial intelligence.<\/h2>\n\n\n\n
Machine learning has its roots deep in statistics.<\/p>\n\n\n\n
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