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All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn't required because complete examples are provided and explained. The novice yearning to break into the data science world, and the enthusiast looking to enrich, deepen, and develop data science skills through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming will make learning easier.

Object-oriented programming (OOP) is the foundation of modern programming languages, including C, Java, C#, Visual Basic .NET, Ruby, and Objective-C.

When you are learning "core" concepts in a programming language, do you frequently make a list of questions to ask? I usually find that I digress a lot. That is, I tend to follow my train of thought down the line until the very end. So I started with concept A about Python, then ended up googling a whole lot about object-oriented programming in Python, which led me to scope out a potential project to do later. Through this process, I bookmarked syntax conventions, object-oriented programming concepts, and a list of frequently used data structures.

A basic understanding of coding concepts and exposure to object-oriented programming is expected.

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Next, we define thelist_images function (line 22–34) which will help us to walk through the input directory structure to grab the image paths. For face detection, we use the Viola-Jones algorithm known as Haar cascade (line 40). It was published in 2001 by Paul Viola and Michael Jones. You can learn more about the algorithm from their original paper Rapid Object Detection using a Boosted Cascade of Simple Features or Wikipedia page. It is quite an old algorithm in the era of deep learning and false-positives prone (reporting a face in the place of the image where no face is present). Even though it is quite easy to use for our demonstrative case. The main processing loop goes as follow: we walk through image files (line 49), read them (line 51) one by one, detect the faces (line 55), save them to the prepared directory (line 59–72) and saving a summary report (line 78–82) with faces coordinates.

W e present an approach to generating natural language justifications of decisions derived from norm-based reasoning. Assuming an agent which maximally satisfies a set of rules specified in an object-oriented temporal logic, the user can ask factual questions (about the agent's rules, actions, and the extent to which the agent violated the rules) as well as "why" questions that require the agent comparing actual behavior to counterfactual trajectories with respect to these rules. To produce natural-sounding explanations, we focus on the subproblem of producing natural language clauses from statements in a fragment of temporal logic, and then describe how to embed these clauses into explanatory sentences. W e use a human judgment evaluation on a testbed task to compare our approach to variants in terms of intelligibility, mental model and perceived trust.

The popularity of data science has often been credited to the increased data availability growing from static to live data, more powerful computing, and an emphasis on analytics-driven decisions made in businesses. The two most popular programming languages for data science are Python and R which are excellent tools in their own right but are very often conceived as rivals in the technological era. There have been many reasons that led to this outlook ranging from the diverse user base to the choice of the programming language used by them. What this leads to is an introduction and premises to the data science language camps the R-camp and the Python camp, each camp supported by a multitude of users who are in a constant duel trying to prove which language is the best. Both R and Python are supreme languages in their own regard, of late, Python has found greater applicability over the years and why is it so?

We introduce agents that use object-oriented reasoning to consider alternate states of the world in order to more quickly find solutions to problems. Specifically, a hierarchical controller directs a low-level agent to behave as if objects in the scene were added, deleted, or modified. The actions taken by the controller are defined over a graph-based representation of the scene, with actions corresponding to adding, deleting, or editing the nodes of a graph. We present preliminary results on three environments, demonstrating that our approach can achieve similar levels of reward as non-hierarchical agents, but with better data efficiency.