It goes without saying that Natural Language Processing, or NLP, is one of the most important and demanded technologies of the present day. You can find it everywhere as people communicate almost everything in language: it is present in web searches, advertisement, emails, customer service, language translation, summaries, etc. On my first acquaintance with NLP, I remember watching an online lecture where a lady was saying: "NLP is easy. Just install the NLTK library and with a minimum experience in Python you can start experimenting". Time flies, as our QA engineers like saying in their test logs.

In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE. After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a spam detector.

Make your first mobile app and game here. Learn how to code and make games in the popular Unreal Engine 4. Learn by building 6 actual games. Make next-level apps that use machine learning with Java, Android, TensorFlow Estimator, PyCharm, and MNIST. By taking this course you will make 3 complete mobile machine learning models and apps. We will build a simple weather prediction project, stock market prediction project, and text-response project.

Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems. In this course you will investigate the challenges of working with large datasets: how to implement algorithms that work; how to use databases to manage your data; and how to learn from your data with machine learning tools. The focus is on practical skills - all the activities will be done in Python 3, a modern programming language used throughout astronomy.

Data analysis is a process for inspecting, consolidating, transforming, and making sense of data in a way that guides the decision-making process. Scala has emerged as an important tool for efficiently performing various data analysis tasks. So, if you're interested to load raw datasets with Spark, and perform exploratory data analysis on those via plotting Scala libraries, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a quick look at your learning journey.

Medical Neuroscience explores the functional organization and neurophysiology of the human central nervous system, while providing a neurobiological framework for understanding human behavior. In this course, you will discover the organization of the neural systems in the brain and spinal cord that mediate sensation, motivate bodily action, and integrate sensorimotor signals with memory, emotion and related faculties of cognition. The overall goal of this course is to provide the foundation for understanding the impairments of sensation, action and cognition that accompany injury, disease or dysfunction in the central nervous system. The course will build upon knowledge acquired through prior studies of cell and molecular biology, general physiology and human anatomy, as we focus primarily on the central nervous system. This online course is designed to include all of the core concepts in neurophysiology and clinical neuroanatomy that would be presented in most first-year neuroscience courses in schools of medicine.

Subtitles are available for Introduction, Case Study and Design Patterns Concepts. Design and architecture of any software/solution provides the base and makes it flexible, extensible for future requirements. A good designed software/solution makes it easy to understand and maintain. Design patterns are known as best practices to design software for problems which are repeated in nature. This course "Design Patterns Through Python" gives you understanding of all 23 patterns described in Gang Of Four book - "Design Patterns: Elements of Reusable Object-Oriented Software", by Gamma, Helm, Johnson and Vlissides.

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The corporate training market is over $200 billion around the world[1] and it's going through a revolution. While we often think of training as programs or courses, a new paradigm has arrived, one I call "Learning in the Flow of Work." The corporate training industry has been around for decades and it has always been impacted by new technology. As the following chart shows, over the last 20 years we've been through four evolutions, each driven by technological and economic change. In the 1970s and 1980s, when I started my career, we learned in classrooms. The technology was slide projectors and "foils" (plastic laminated slides).

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: - A basic understanding of linear algebra and multivariate calculus. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.