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.

If you haven't encountered any reviews of Harvard psychologist Steven Pinker's new bestseller Enlightenment Now--which would be amazing, given how many there have been--don't worry. I can summarize them in two paragraphs. The positive ones say Pinker argues convincingly that we should be deeply grateful for the Enlightenment and should put our stock in its legacy. A handful of European thinkers who were born a few centuries ago set our species firmly on the path of progress with their compelling commitment to science, reason, and humanism (where humanism means "maximizing human flourishing"). Things have indeed, as Pinker documents in great detail, gotten better in pretty much every way--materially, morally, politically--since then. And if we stay true to Enlightenment values, they'll keep getting better.

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.

Keras is a high-level neural network library written in Python, and runs on top of either Theano or TensorFlow. It is a minimal, highly modular framework that runs on both CPUs and GPUs, and allows you to put your ideas into action in the shortest possible time. This course will help you get started with the basics of Keras, in a highly practical manner. Antonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, innovation, and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing.

In this Course you learn k-Nearest Neighbors & Naive Bayes Classification Methods. In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. The k-NN algorithm is among the simplest of all machine learning algorithms. For classification, a useful technique can be to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. The neighbors are taken from a set of objects for which the class (for k-NN classification).

The level in this article is for college students familiar with calculus, This material will be also of interest to college professors looking for new material to teach, or for original exam questions, as well as for business data scientists with some spare time, interested in refreshing their math skills. The problems cover real analysis, mathematical algorithms and numerical precision, correct visualizations, as well as geometry. The third problem is the most interesting one in my opinion, and could become a subject of active mathematical research with one new great, unsolved conjecture being proposed, of a probabilistic nature. The last problem has many applications in engineering science. Note the alternance of the signs.

Neural Networks are at the forefront of almost all recent major technology breakthroughs. The intersection of big data, parallel programming, and AI generated a new wave of Neural Network research. In this course, you will be taken through some of the best uses of Neural Networks using TensorFlow. You'll explore Deep Reinforcement Learning algorithms such as Generative Networks and Deep Q Learning. You will learn to implement some more complex types of neural networks such as Deep Q Learning with OpenAI Gym, autoencoders, and Siamese neural networks.

Welcome to Managing Big Data on Google's Cloud Platform. This is the second 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. At this juncture the Google Certified Data Engineer is the only real world certification for data and machine learning engineers. NOTE: This is NOT a course on Big Data.

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.