In this course, we cover two analytics techniques: Descriptive statistics and Predictive analytics. For the predictive analytic, our main focus is the implementation of a logistic regression model a Decision tree and neural network. We well also see how to interpret our result, compute the prediction accuracy rate, then construct a confusion matrix . By the end of this course, you will be able to effectively summarize your data, visualize your data, detect and eliminate missing values, predict futures outcomes using analytical techniques described above, construct a confusion matrix, import and export a data.

If you want to learn how to perform real advanced statistical analyses in the R program, you have come to the right place. Now you don't have to scour the web endlessly in order to find how to do an analysis of covariance or a mixed analysis of variance, how to execute a binomial logistic regression, how to perform a multidimensional scaling or a factor analysis. Everything is here, in this course, explained visually, step by step. So, what's covered in this course? First of all, we are going to study some more techniques to evaluate the mean differences.

Several use cases showcasing different solutions are covered in this video. Introduction to Amazon SageMaker (10 minutes) This course provides an overview of Amazon SageMaker, a fully managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models. Introduction to AWS Greengrass (10 minutes) This course is an introduction to AWS Greengrass, which lets you run local compute, messaging, data caching, and sync capabilities for connected devices in a secure way. You can build machine learning models in the cloud and execute their inference at the edge. Introduction to Amazon Comprehend (10 minutes) This course introduces you to Amazon Comprehend, a new AWS service that helps with natural language processing. In this course, we discuss how Amazon Comprehend solves challenges like the exponential growth of unstructured text, explore the service's five main capabilities, and review some popular use cases.

We will begin this course with a tutorial on the standard camera models used in computer vision. These models allow us to understand, in a geometric fashion, how light from a scene enters a camera and projects onto a 2D image. By defining these models mathematically, we will be able understand exactly how a point in 3D corresponds to a point in the image and how an image will change as we move a camera in a 3D environment. In the later modules, we will be able to use this information to perform complex perception tasks such as reconstructing 3D scenes from video.

Knowledge of Geographic Information Systems (GIS) is an increasingly sought after skill in industries from agriculture to public health. This Specialization, offered in partnership with ArcGIS developer Esri, will teach the skills you need to successfully use GIS software in a professional setting. You will learn how to analyze your spatial data, use cartography techniques to communicate your results in maps, and collaborate with peers in GIS and GIS-dependent fields. In the final Capstone Project, you will create a professional-quality GIS portfolio piece using a combination of data identification and collection, analytical map development, and spatial analysis techniques.

Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!

We just unlocked a Free Preview of our Machine Learning Engineer Nanodegree Program! Discover amazing new content, and explore your future in Machine Learning, today! The Machine Learning Engineer Nanodegree program has been one of Udacity's benchmark programs for over 2 years. Thousands of students have graduated the program, and many have gone on to great careers at companies like Google, Amazon, and more. As technology evolves, so does our curriculum, and we think much of the program's success can be attributed to keeping the content up-to-the-minute current.

These days, I am sure 90% of LinkedIn traffic contains one of these terms: DS, ML or DL -- acronyms for Data Science, Machine Learning or Deep Learning. Beware of the cliche though: "80% of all the statistics are made on the spot". If you blinked on these acronyms perhaps you need to google a bit and then continue reading the rest of this post. This post has 2 goals. First, it attempts to put all the fellow Data Science learners at ease.

This list can provide inspiration for which courses you might consider for yourself in the coming year, but does it also say anything about the evolution of MOOCs? Are there trends in which types of courses are becoming more popular? And what are the reasons behind those movements? I reached out to a Coursera spokesperson to find out. As you probably already noticed, tech is booming. The subject is "continuing to draw significant interest: artificial intelligence, blockchain, and anything at the intersection of business and data analytics continue to see growth," Coursera confirmed.

The seeds were sown back in the 1950s when the first artificial neural network was created. Since then, progress has been rapid, with the structure of the neuron being "re-invented" artificially. Computers and mobiles have now become powerful enough to identify objects from images. Not just images, they can chat with you as well! That's not all--they can drive, make supersonic calculations, and help businesses solve the most complicated problems (more users, revenue, etc). But, what is driving all these inventions? With increasing open source contributions, R language now provides a fantastic interface for building predictive models based on neural networks and deep learning.