What is Machine Learning and How Does It Work? In-Depth Guide

They are extremely useful for blocking an unauthorized transaction in the banking context, and equally useful when monitoring natural phenomena, such as with earthquakes and hurricanes. In this case our algorithms do not need to have access to the our dataset, and therefore only need a feature set X. These solutions can be more or less accurate, and it is difficult to reach performances that are comparable to human ones. Explaining what machine learning is relatively simple, but the discussion must be calibrated according to the interlocutor.

If a computer can beat a human at a strategic game like chess, how much can we infer about its ability to reason strategically in other environments? For a long time, the answer was, “very little.” After all, most board games involve a single player on each side, each with full information about the game, and a clearly preferred outcome. Yet most strategic thinking involves cases where there are multiple players on each side, most or all players have only limited information about what is happening, and the preferred outcome is not clear. For all of AlphaGo’s brilliance, you’ll note that Google didn’t then promote it to CEO, a role that is inherently collaborative and requires a knack for making decisions with incomplete information. Initially, programmers tried to solve the problem by writing programs that instructed robotic arms how to carry out each task step by step. However, just as rule-based NLP can’t account for all possible permutations of language, there also is no way for rule-based robotics to run through all the possible permutations of how an object might be grasped.

Getting Started with Machine Learning

Neural networks, whose structure is loosely inspired by that of the brain, are interconnected layers of algorithms, called neurons, which feed data into each other, with the output of the preceding layer being the input of the subsequent layer. The final 20% of the dataset is then used to test the output of the trained and tuned model, to check the model’s predictions remain accurate when presented with new data. A good way to explain the training process is to consider an example using a simple machine-learning model, known as linear regression with gradient descent. In the following example, the model is used to estimate how many ice creams will be sold based on the outside temperature.

Developed by Yann LeCun and others, CNNs don’t try to understand an entire image all at once, but instead scan it in localized regions, much the way a visual cortex does. LeCun’s early CNNs were used to recognize handwritten numbers, but today the most advanced CNNs, such as capsule networks, can recognize complex three-dimensional objects from multiple angles, even those not represented in training data. Meanwhile, generative adversarial networks, the algorithm behind “deep fake” videos, typically use CNNs not to recognize specific objects in an image, but instead to generate them. Explaining how a specific ML model works can be challenging when the model is complex.

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Deep learning can ingest unstructured data in its raw form (e.g., text or images), and it can automatically determine the set of features which distinguish different categories of data from one another. This eliminates some of the human intervention required and enables the use of larger data sets. You can think of deep learning as “scalable machine learning” as Lex Fridman notes in this MIT lecture (link resides outside ibm.com). Well because the logic of these algorithms is completely different compared to the supervised ones. Not all machine learning models have to behave like the child in the metaphor. In fact, unsupervised learning algorithms try to discover hidden patterns in the data to group, separate or manipulate the data in some way.

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