Assume that you have a dataset of images of animals such as cats and dogs, and you want to have the software or an application that can recognize and differentiate them. The first thing that you have to do here is to interpret the images as a set of feature sets. For example, does the image show the animal’s eyes? If so, what are their sizes? Does it have ears? What about tails? How many legs? Does it have wings?
Prior to machine learning, each image would be transformed to a vector of features. Then traditionally, we had to write down some rules or methods in order to get computers to be intelligent and detect the animals. But, it was a failure. Why? Well as you can guess, it needed a lot of rules, highly dependent on the current dataset, and not generalized enough to detect out of sample cases. This is when machine learning entered the scene. Using machine learning, allows us to build a model that looks at all the feature sets, and their corresponding type of animals, and it learns the pattern of each animal.
It is a model built by machine learning algorithms. It detects without explicitly being programmed to do so. In essence, machine learning follows the same process that a 4 years old child uses to learn, understand and differentiate animals. So, machine learning algorithms, inspired by the human learning process, iteratively learn from data, and allow computers to find hidden insights. These models help us in a variety of tasks, such as object recognition, summarization, recommendation and so on.
Wow. Beautifully written article, awesome job; keep up the great work.
Thanks
This is a good and concise article, can you suggest where I can check to finds more practical examples of the various Machine Learning Techniques?
I am planning to create machine learning tutorial with examples in the future. But For now, I can suggest you some websites that you can find and practice machine learning techniques.
– Google Colab :
Google Colaboratory is a platform built on top of the Jupyter Notebook environment which runs entirely on Google Cloud Platform (GCP). This platform provides GPU which is free of cost and supports Python 2 and 3 versions. With the help of Colab, one can not only improve machine learning coding skills but also learn to develop deep learning applications. You can also learn to work with popular deep learning libraries such as Keras, TensorFlow, OpenCV and others.
– Kaggle :
Kaggle is a no-setup, customisable, Jupyter Notebooks environment by Google. This platform is very much similar to Google Colab in the aspect that both the platforms provide free GPUs along with a large community of published data and code.
– Open ML :
OpenML is an open, collaborative, and automated machine learning environment which includes several specific features such as find or add data to analyse, download or create computational tasks, find or add data analysis flows and much more.
Curious Question: What is the difference between Artificial Intelligence and Machine Learning?
Hi Gloria
In computer science, the term artificial intelligence (AI) refers to any human-like intelligence exhibited by a computer, robot, or other machine. In popular usage, artificial intelligence refers to the ability of a computer or machine to mimic the capabilities of the human mind—learning from examples and experience, recognizing objects, understanding and responding to language, making decisions, solving problems—and combining these and other capabilities to perform functions a human might perform, such as greeting a hotel guest or driving a car.
Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so. Look at this image for this question.
The easiest way to understand the relationship between artificial intelligence (AI), machine learning, and deep learning is as follows:
– Think of artificial intelligence as the entire universe of computing technology that exhibits anything remotely resembling human intelligence. AI systems can include anything from an expert system—a problem-solving application that makes decisions based on complex rules or if/then logic—to something like the equivalent of the fictional Pixar character Wall-E, a computer that develops the intelligence, free will, and emotions of a human being.
– Machine learning is a subset of AI application that learns by itself. It actually reprograms itself, as it digests more data, to perform the specific task it’s designed to perform with increasingly greater accuracy.
– Deep learning is a subset of machine learning application that teaches itself to perform a specific task with increasingly greater accuracy, without human intervention.