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Machine Learning

  • The term “fairness” is used frequently in artificial intelligence (AI) and machine learning (ML). “Fairness” is a critical component of most responsible and ethical AI principles. But what does that imply in practice, and what constitutes a “fair” machine learning system?

  • Examples of machine learning and deep learning are all around us. When you imagine how Facebook can recognize if the person in the photo is the legit owner or the emerging self-driving cars or how Netflix can make predictions of movies, you cannot avoid the two topics. Artificial intelligence is a rather wide subject, and therein is Machine Learning. In fact, do not worry if you are already finding it confusing. We all were at some point. Now let’s simplify this by breaking it down for you.

  • There are key concepts that lay the foundation in machine learning algorithms for understanding the theme. One of these concepts is a dataset, an assortment of cases that support machine learning techniques for various purposes. A dataset is sometimes known as a validation or training dataset that is often fed into the machine learning system to train models.

  • Let’s take machine learning as an application of artificial intelligence that automatically gives the system the ability to learn and make some improvements without actual programming. This learning mainly focuses on specific computer programs that can access data and use it for their learning. In this article, we examine possible questions that you can be asked or can be tested in a machine learning interview.

  • To most people, understanding the recent development in artificial intelligence (AI) can be irresistible, but if it’s learning the fundamentals you are concerned with, you can limit several AI inventions into two overlapping concepts; Machine learning and deep learning. These concepts are arguably interchangeable, creating an inapt misunderstanding in its use.

  • Due to technological advancements, the quest to automate and improve the way tasks are done at the forefront. Artificial language (AI) and Machine Language (ML) are taking over. When covering this topic, you will deal with and relate with lots of data. Datasets have been proven to be the easiest way to learn machine learning and data science. Therefore, this article will cover the best public datasets for machine learning and data science.

  • In computer science, regularization is a concept about the addition of information with the aim of solving a problem that is ill-proposed. It is also an approach that helps address over-fitting. In Machine Learning, regularization refers to part or all modifications done on a machine-learning algorithm to minimize its generalization error. However, it does not include the training error.

  • Although it might not be that apparent, machine learning algorithms are set to change the landscape of several different industries by automating the tasks currently being performed by human beings. Considering this, data scientists delve deeper into discovering more powerful ML algorithms each day.

  • Machine learning is about extracting knowledge from data. It is basically a research field at the intersection of statistics, Artificial Intelligence, and Computer Science and is sometimes referred to as Predictive analytics or Statistical Learning. A Machine Learning system is trained rather than being explicitly programmed, which means it evolves over time.