Machine Learning (ML)

What is Machine Learning?

Machine Learning (ML) is a method stemming from a branch within artificial intelligence that enables machines to make decisions or predictions based on data rather than a predefined algorithm. A widely known example of applied machine learning is Gmail Inbox’s Smart Reply system, assisting people by automating 10% of their email responses.

It is based on the foundation that systems can automatically learn from sample data sets (known as “training data”) to characterize patterns that help create predictions and fashioning decisions with minimal human intervention.

The Evolution of AI and Machine Learning 

Computing technologies have taken monumental strides over the past few decades, creating a colossal chasm between the machine learning of the past and today’s technological capabilities.

Its conception was founded on researchers’ interest in artificial intelligence and whether or not computers could learn from data without explicitly being programmed to perform specific tasks. The iterative nature rooted within machine learning’s pattern recognition system plays a critical role in its ability to adapt itself independently. The technology learns from previous computations to construct recurrent, trustworthy decisions and results. 

Although these machine learning algorithms have been around for quite some time, the development to automatically apply compound mathematical calculations to big data is a novel phenomenon. An example of such a phenomenon is ‘Amazon Go’ and its ability to ‘see’ what a person takes and charge them for it by using deep learning, machine learning’s newest approach. 

Types of Machine Learning Models

There are a four widely known machine learning models:


Classification is a method of categorizing data by analyzing the data and drawing conclusions on how each entity in the data should be tagged and defined. For example, in a dataset containing different types of flowers, machine learning can be used to classify each type into different categories.


Simply put, regression paradigms measure the relationships between variables to predict a continuous (numerical) value. Regression models specifically know how to provide a number estimate instead of a class estimate. For example, a typical case of a regression model in action is the prediction of housing prices, when given features such as size. 


Clustering is a technique that aims to find natural groups in a space and interpret the data.

Recommender Systems

This is a technique that enables the system to suggest relevant items to individuals based on their previous selections. For example: The algorithm Netflix uses to recommend movies we would like to watch, based on movies we’ve previously watched.

Methods of Machine Learning

There are four methods of training a model, however the two most commonly used ones are supervised learning and unsupervised learning.

Supervised learning

The method of supervised learning in machine learning uses labeled datasets to train algorithms to classify data based on patterns detected within the sets in order to predict outcomes accurately. The programmer has to explicitly define which features the machine learning model should learn to predict. For example, if you want to build a model that tries to predict the price of a house, and you explicitly specify the area of the house to the model, you are doing supervised learning.

Unsupervised learning

The method of unsupervised learning in machine learning uses algorithms to sift through datasets and organize unlabeled data. These algorithms do not need to be explicitly programmed to look at specific features of the data. Instead, they are able to independently find and make sense of hidden patterns by deciphering similarities and differences within the data. For example: segmenting customers in marketing data so that marketers can target them differently, without explicitly defining the groups of customers.

Semi-supervised learning

Semi-supervised learning is somewhat of an amalgamation of the two previously mentioned machine learning methods. On the one hand, datasets must solve classification issues using supervised learning algorithms. On the other hand, the model should know to tag information without being explicitly trained to do so; a task accomplished using unsupervised machine learning techniques.

Semi-supervised learning combines clustering and classification algorithms (techniques executed by supervised and unsupervised learning, respectively). Clustering the data will organize them based on similarities and differences. Once distinguishable, the data is labeled to be used to train the machine learning model for classification.

Reinforcement learning 

Currently, one of the more hyped machine learning paradigms is reinforcement learning. Reinforcement learning is the technique in which an agent (in our case, bots) learns within an interactive environment to make a sequence of decisions through trial and error. For the system to learn, penalties and rewards are given based on the positive or negative impact their decisions generate on the overall task at hand. The challenge that arises through the employment of this paradigm involves the trade-off between exploitation and exploration. The ‘agent’ must exploit the information it knows to receive an award yet seek to explore the possibility of improving itself by executing actions differently. Neither of these cases can be pursued without eliminating the likelihood of failure. The agent must try various actions and learn to favor those that work best through its supervisor’s positive reinforcements (the programmer).

A good example of reinforcement learning is the ability of a model to predict stock prices, by rewarding the algorithm automatically every time it does a good job in predicting the future value of a stock, and penalizing it otherwise.

Why is ML essential within conversational AI?

Due to our data-driven culture, we are continuously being inundated by an overwhelming influx of information. Nevertheless, the cost of computational processes has been decreasing parallelly to this, facilitating the production of models capable of taking on larger, more complex datasets that deliver accurate results quicker than ever before. Also, because of the decreased cost surrounding computational processes, much more research is being carried out on the topic and many more methods are being explored to evolve the technology.

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