Your first step towards Machine Learning… | AIMS #0.0.1 [Artificial Intelligence Made Simple!]

Machine learning (ML) is said to be one of the most critical skills of current times. From autonomous vehicles to self-tuned databases, ML is found everywhere. Industry analysts often refer to ML-driven automation as the job killer. Almost every domain and industry vertical are getting impacted by ML. Platform companies with massive investments in Artificial Intelligence (AI) research are shipping new tools and frameworks at a rapid pace.

All the above factors have put an average developer under pressure to acquire machine learning skills. There is a sudden rush to learn the tools and technologies related to ML. The number of self-paced courses and MOOCs has doubled in 2019. In emerging markets like India, there are many technical training institutes promising to transform programmers into data scientists.

So, What are Artificial Intelligence, Machine Learning and Deep Learning?

The term “Artificial Intelligence” has been floating around for a while. We see it in sci-fi movies, “AI” game bots we play against, Google search, and, oh yeah, those robots that are someday going to take over the world. Off late, though, “Machine Learning” and “Deep Learning” have surfaced, with many asking what exactly each of these are. All three are part of the reason why AlphaGo trounced Lee Se-Dol. But they are not the same things.

The easiest way to think of their relationship is to visualize them as concentric circles with AI — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s AI explosion — fitting inside both.

AI is defined as “the science and engineering of making intelligent machines”. There are a lot of ways to simulate human intelligence, and some methods are more intelligent than others.

AI can be a pile of if-then statements, or a complex statistical model mapping raw sensory data to symbolic categories. The if-then statements are simply rules explicitly programmed by a human hand. Agents that fall under AI but not Machine Learning are generally agents that solely utilize decision trees for logic, or agents built with rules and instructions.

Arthur Samuel coined the phrase “Machine Learning” in 1959, defining it as “the ability to learn without being explicitly programmed.”

Machine learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task.

Machine learning is a subset of AI. That is, all machine learning counts as AI, but not all AI counts as machine learning. For example, symbolic logic — rules engines, expert systems and knowledge graphs — could all be described as AI, and none of them are machine learning. One aspect that separates machine learning from the knowledge graphs and expert systems is its ability to modify itself when exposed to more data; i.e. machine learning is dynamic and does not require human intervention to make certain changes. That makes it less brittle, and less reliant on human experts.

Deep learning was inspired by the structure and function of the brain, namely the interconnecting of many neurons. Usually, when people use the term deep learning, they are referring to deep artificial neural networks. Neural networks are algorithms inspired by our understanding of the biology of our brains — all those interconnections between the neurons. But, unlike a biological brain where any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have discrete layers, connections, and directions of data propagation.

Deep Learning is basically Machine Learning on steroids. There are multiple layers to process features, and generally, each layer extracts some piece of valuable information. So you could apply the same definition to deep learning that Arthur Samuel did to machine learning — a “field of study that gives computers the ability to learn without being explicitly programmed” — while adding that it tends to result in higher accuracy, require more hardware or training time, and perform exceptionally well on machine perception tasks that involved unstructured data such as blobs of pixels or text. We will discuss more about this in the upcoming articles.

Too Many Learning Resources

The number of self-paced courses and massive open online courses (MOOC) exploded in the recent past. There are dozens of courses available for developers to learn data science and machine learning.

But the choice of these courses leads to confusion. Given how vast the ML domain is, no course is complete. The tools and frameworks are rapidly evolving making these courses outdated.

Social media and blogosphere are full of articles, tutorials and guides related to ML. The challenge with this is that most of them are incomplete leaving the essential part to the imagination of the developer. So, that’s why I have started this series of articles on learning ML with the latest and best of the tools and frameworks.

Index

AIMS #0… — Definitions and terminology guides for AI, ML and DL.

AIMS #1.1.. — Python and its libraries for Data Science Fast Track Guides

AIMS #1.2.. —R for Data Science Fast Track Guides

AIMS #2.1.. — Machine Learning with Python Fast Track Guides

AIMS #2.2.. —Machine Learning with R Fast Track Guides

AIMS #3.1.. — Deep Learning with TensorFlow Fast Track Guides

AIMS #3.2.. — Deep Learning with Pytorch Fast Track Guides