This course is a comprehensive, practical guide to using MongoDB and Spark in Python, learning how to store and make sense of huge data sets, and performing basic machine learning tasks to make predictions. MongoDB is one of the most powerful non-relational database systems available offering robust scalability and expressive operations that, when combined with Python data analysis libraries and distributed computing, represent a valuable set of tools for the modern data scientist. NoSQL databases require a new way of thinking about data and scalable queries. Once Mongo queries have been mastered, it is necessary to understand how we can leverage this API in Python's rich analysis and visualization ecosystem. This course will cover how to use MongoDB, particularly if you are used to SQL databases, with a focus on scalability to large datasets.

This course was funded by a wildly successful Kickstarter. Do you want to predict the stock market using artificial intelligence? In this course, you learn how to code in Python, calculate linear regression with TensorFlow, and make a stock market prediction app. We interweave theory with practical examples so that you learn by doing. AI is code that mimics certain tasks.

If you want to build sophisticated and intelligent mobile apps or simply want to know more about how machine learning works in a mobile environment, this course is for you. There are next to no courses on big platforms that focus on mobile machine learning in particular. All of them focus specifically on machine learning for a desktop or laptop environment. We provide clear, concise explanations at each step along the way so that viewers can not only replicate, but also understand and expand upon what I teach. Other courses don't do a great job of explaining exactly what is going on at each step in the process and why we choose to build models the way we do.

Get your team access to Udemy's top 2,500 courses anytime, anywhere. The main purpose of this course is to give students the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning and big data. He is the South East Asia s First XNA/DirectX MVP (Most Valuable Professional). He had delivered sessions at various events and conferences in Hong Kong, Malaysia, Singapore, Sri Lanka and India.

This five-week, accelerated online specialization provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.

MATLAB is the language of choice for many researchers and mathematics experts for Machine Learning. This video course will help you build a foundation in Machine Learning using MATLAB. You'll start by performing data fitting, pattern recognition, and clustering analysis. Then, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. Finally, you will learn to put it all together through real-world cases covering major Machine Learning algorithms and will now be an expert in performing Machine Learning with MATLAB.

Welcome to Tensorflow on the Google Cloud Platform for Data Engineers This is the fourth course in a series of courses designed to help you attain the coveted Google Certified Data Engineer. Additionally, the series of courses is going to show you the role of the data engineer on the Google Cloud Platform. NOTE: This is not a course on how to develop machine learning models with TensorFlow. This is a very targeted course on TensorFlow for data engineers. My goal is to give data engineers what they need to know for the exam and provide learners with the foundations of TensorFlow on Google's Cloud Platform.

What if you could build a character that could learn while it played? Think about the types of game play you could develop where the enemies started to outsmart the player. This is what machine learning in games is all about. In this course we will discover the fascinating world of artificial intelligence beyond the simple stuff and examine the increasingly popular domain of machines that learn to think for themselves. In this course, Penny introduces the popular machine learning techniques of genetic algorithms and neural networks using her internationally acclaimed teaching style and knowledge from a Ph.D in game character AI and over 25 years experience working with games and computer graphics.

Gain hands-on experience building a machine learning system and have a complete project you can add to your online data science portfolio, and code you can adapt to just about any machine learning problem you may encounter. This course is a comprehensive, foundational data science course where you'll learn everything you need to know to get started in data science. In this course you play the role of a data scientist for an online retailer, tasked with identifying sales trends and predicting future sales.

Data Scientists with Natural Language Processing & Text Mining Skills are the Hottest and Most In-Demand Job Applicants Today! Data Scientist was recently dubbed "The Sexiest Job of the 21st Century" by Harvard Business Review, Glassdoor reports that Data Scientist was named the "Best Job in America for 2016," and business media from Forbes to The New York Times frequently report about the increasing demand for data scientists. Most of this boom is using data that is organized and structured from your databases and spreadsheets but a huge opportunity awaits from the untapped unstructured text data (aka tweets, Facebook posts, blog posts, comments, SMS, chats, voice transcripts, etc.). Within the data science field, natural language processing is an extremely hot area in academia, startups and is just being started to be used widely within the mainstream of corporate America. Data Scientist job posting with natural language processing skills roughly doubled in 2016.