This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling).

We propose a framework for ensuring safe behavior of a reinforcement learning agent when the reward function may be difficult to specify. In order to do this, we rely on the existence of demonstrations from expert policies, and we provide a theoretical framework for the agent to optimize in the space of rewards consistent with its existing knowledge. We propose two methods to solve the resulting optimization: an exact ellipsoid-based method and a method in the spirit of the "follow-the-perturbed-leader" algorithm. Our experiments demonstrate the behavior of our algorithm in both discrete and continuous problems. The trained agent safely avoids states with potential negative effects while imitating the behavior of the expert in the other states.

Welcome to the course "Python: Solved Interview Questions on Algorithms and Data structures". We would have observed the fact that though most of us are developers, only few would get a chance to work on certain advanced programming stuff like Data Structures, Linked Lists, Trees. The rest of us get to spend time in Bug fixing, resolving Maintenance issues during our work hours. Though this work doesn't help us much in improving our learning curve, it certainly feeds us and our families. So, Keeping this in mind, at the work place, We don't have any option but to work honestly.

About this course: This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. This is the fifth and final course of the Deep Learning Specialization. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.

Are You an Ecologist or Conservationist Interested in Learning GIS and Machine Learning in R? Then this course is for you! I will take you on an adventure into the amazing of field Machine Learning and GIS for ecological modelling. You will learn how to implement species distribution modelling/map suitable habitats for species in R. My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals.

Helsinki University in Finland has launched a course on artificial intelligence -- one that's completely free and open to everyone around the world. Unlike Carnegie Mellon's new undergrad degree in AI, which the institution created to train future experts in the field, Helsinki's offering is more of a beginner course for those who want to know more about it. A lot of tech giants like Google now have divisions working on artificial intelligence projects, and even whole non-tech industries already depend on AI for various tasks. But as Janina Fagerlund from the university's project partner (tech strategy firm Reaktor) said, people might not know that their lives are already affected by AI every day. Fagerlund mentioned the use of AI in the food industry to sort produce and other items at facilities as an example.

Artificial Intelligence is one of the hottest fields in computer science right now and has taken the world by storm as a major field of research and development. Python has surfaced as a dominant language in AI/ML programming because of its simplicity and flexibility, as well as its great support for open source libraries such as Scikit-learn, Keras, spaCy, and TensorFlow. If you're a Python developer who wants to take first steps in the world of artificial intelligent solutions using easy-to-follow projects, then go for this learning path. This comprehensive 2-in-1 course is designed to teach you the fundamentals of deep learning and use them to build intelligent systems. You will solve real-world problems such as face detection, handwriting recognition, and more.

Update on April 18th, 2018: (a) New coding exercise has been added to Collections Framework chapter to test Lists & Queues!, (b) New assignment has been added in Section 3 "This is by far the best advanced as well as beginner course I have ever read/seen since Andre LaMothe quit writing." This one should be the best seller of all the other ... " Brady Adams "This is THE best course on Java on Udemy - Period! Dheeru is not only passionate about what he is coaching but also OBSESSIVE and covers every minute detail of the subject ... Most lessons have demos which Dheeru makes sure that they do work without any glitches. He is a genius coder ... Plus, he bases the course on the best practices from the book "Effective Java" which is great. You get to cover most of this book if you study this course! If you want to learn Java right from installing, configuring and all the way to mastering its advanced topics - look no further - you are at the right place THIS - IS - IT!!!" Richard Reddy "This is a wonderful course.

Real-World Scala leads developers in the right direction for their future endeavors with Scala. The lesson covers some of the more intimidating aspecs of Scala including implicits and Java interoperability. The lesson also covers common "gotchas" in pattern matching and type inference and provides general advice for code readability.