Python Regular Expressions is a hands-on course that teaches you everything you need to know about Regular Expression using Python Language. Regular Expression is a powerful text processing tool for log mining, data parsing, cleanup and preparation. Power and elegance of regular expression allows you to do complex data extraction and cleanup with very few lines of code. Over 60% of the effort in big data projects is spent on data cleanup and preparation. Data can come from variety of sources including internal databases, log files, sensor generated data, Twitter, Facebook and so forth.

We introduce a new neural network model, together with a tractable and monotone online learning algorithm. Our model describes feed-forward networks for classification, with one output node for each class. The only nonlinear operation is rectification using a ReLU function with a bias. However, there is a rectifier on every edge rather than at the nodes of the network. There are also weights, but these are positive, static, and associated with the nodes. Our "rectified wire" networks are able to represent arbitrary Boolean functions. Only the bias parameters, on the edges of the network, are learned. Another departure in our approach, from standard neural networks, is that the loss function is replaced by a constraint. This constraint is simply that the value of the output node associated with the correct class should be zero. Our model has the property that the exact norm-minimizing parameter update, required to correctly classify a training item, is the solution to a quadratic program that can be computed with a few passes through the network. We demonstrate a training algorithm using this update, called sequential deactivation (SDA), on MNIST and some synthetic datasets. Upon adopting a natural choice for the nodal weights, SDA has no hyperparameters other than those describing the network structure. Our experiments explore behavior with respect to network size and depth in a family of sparse expander networks.

Reinforcement learning (RL) has had many successes in both "deep" and "shallow" settings. In both cases, significant hyperparameter tuning is often required to achieve good performance. Furthermore, when nonlinear function approximation is used, non-stationarity in the state representation can lead to learning instability. A variety of techniques exist to combat this --- most notably large experience replay buffers or the use of multiple parallel actors. These techniques come at the cost of moving away from the online RL problem as it is traditionally formulated (i.e., a single agent learning online without maintaining a large database of training examples). Meta-learning can potentially help with both these issues by tuning hyperparameters online and allowing the algorithm to more robustly adjust to non-stationarity in a problem. This paper applies meta-gradient descent to derive a set of step-size tuning algorithms specifically for online RL control with eligibility traces. Our novel technique, Metatrace, makes use of an eligibility trace analogous to methods like $TD(\lambda)$. We explore tuning both a single scalar step-size and a separate step-size for each learned parameter. We evaluate Metatrace first for control with linear function approximation in the classic mountain car problem and then in a noisy, non-stationary version. Finally, we apply Metatrace for control with nonlinear function approximation in 5 games in the Arcade Learning Environment where we explore how it impacts learning speed and robustness to initial step-size choice. Results show that the meta-step-size parameter of Metatrace is easy to set, Metatrace can speed learning, and Metatrace can allow an RL algorithm to deal with non-stationarity in the learning task.

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.

Artificial Intelligence (AI) is transforming many human activities ranging from daily chores to highly sophisticated tasks. But unlike many other industries, the higher education sector has yet to be really influenced by AI. Uber has disrupted the taxi sector, Airbnb has disrupted the hotel industry and Amazon disrupted first the bookselling sector, then the whole retail industry. It is only a matter of time then until the higher education sector undergoes a significant transformation. Within a few short years, universities may well have changed beyond all recognition.

The company you work for has accumulated a lot of valuable data from its customers, all stored as text, and you need to extract some value from that data. You've spent a lot of combined time writing about what they want but no-one knows what they have written about and no-one has the time to read all the messages. This course empowers you to know how to attack this and other text analysis problems to unlock value for your organization. You'll start by seeing how NLP can help you extract useful information from large collections of text data, and how you can use the latest Python libraries for NLP. Then we'll show you how to solve a practical problem using NLP by building a spam SMS detector. You'll also learn to convert words into numbers that can be analyzed.

Machine learning, neural networks, deep learning, and artificial intelligence are all around us, and they're not going away. I will show you how to get a grasp on this ever-growing technology in this course. This course was funded by a wildly successful Kickstarter! With this course I will help you understand what machine learning is and compare it to Artificial Intelligence (AI). Together we will discover applications of machine learning and where we use machine learning daily.

About this course: Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study.

Be an awesome Python Programmer now & Learn one of the most sought-after programming language by employers in 2017!. This course takes you from the very beginning steps and basics of Python until you become comfortable in using the language. We are going to first start by learning basic syntax in Python and learn the concepts of programming in Python like if statements and conditions, variables, for/while loops & functions. Then we move to the Object Oriented Programming & Learn Classes & Objects, which is probably the most important concept in all programming languages. We finally learn how to import packages in Python & use them to build our awesome apps that solves real problems.

Python is a dynamic modern object -oriented programming language. It is easy to learn and can be used to do a lot of things both big and small. Python is what is referred to as a high level language. Python is used in the industry for things like embedded software, web development, desktop applications, and even mobile apps! SQL-Lite allows your applications to become even more powerful by storing, retrieving, and filtering through large data sets easily.