This five-course accelerated specialization is designed for data professionals who are responsible for designing, building, analyzing, and optimizing big data solutions. Through a combination of video lectures, quizzes, and hands-on labs, you'll learn how to carry out serverless data analysis and productionize machine learning models. This specialization is designed to give participants a robust hands-on experience and is primarily lab-focused. Learn how to deliver business value with Big Data and Machine Learning Solutions on Google Cloud Platform. To get up to speed quickly, follow the courses in this specialization.

This course is designed to provide a complete introduction to Deep Learning. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems. We start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then we introduce Artificial Neural Networks and explain how they are trained to solve Regression and Classification problems. Over the rest of the course we introduce and explain several architectures including Fully Connected, Convolutional and Recurrent Neural Networks, and for each of these we explain both the theory and give plenty of example applications.

Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models and TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. This course is your guide to exploring the possibilities with deep learning; it will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With this video course, you will dig your teeth deeper into the hidden layers of abstraction using raw data.

SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. In Spark 2.0.2, SparkR provides a distributed data frame implementation that supports operations like selection, filtering, aggregation etc. (similar to R data frames) but on large datasets. SparkR also supports distributed machine learning using MLlib. You will learn how to create spark cluster in Databricks. You will learn how to create dataframes and grouping data and aggregating data.

In this day and age of digital marketing, having the right visual content can increase the brand value significantly. But not everyone is equipped to create professional graphics and videos on their own. This means people spend thousands of dollars in hiring designers or buying expensive softwares, which have deep learning curves and are not easy to use. The solution would be to use readily-available tools like PowerPoint or use free online tools which are easy to use. With Visual Deck, you can learn to create your own professional graphics, presentations and animated videos with no prior experience whatsoever!

You will engage in 5 hands-on labs for creating advanced models …. Because our goal is to get you up to speed as quickly as possible, we'll cover through the 30 lectures, The course will enable you to extract actionable insight from your customers' data According to a Mckinsey Study, demand for data scientists is projected to exceed supply by more than 50% by 2018. That's the gap you as citizen Data Scientists are going to fill Citizen data scientist are "Business people with the right attitude - curious, adventurous, determined - to research and improve things in the organization" SAS The need is so, that according to Gartner, by 2017, the number of citizen data scientists will grow 5 times faster than the number of highly skilled data scientists. This course is designed for business professionals: marketer, manager and analytical minds in every department … who want to take their skills to the next level. If you have solid business knowledge, curious, determined to improve things in your company or just willing to learn new methods and tools than this course is for you….

Are you a data analyst, data scientist, or a researcher looking for a guide that will help you increase the speed and efficiency of your machine learning activities? If yes, then this course is for you! Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. It has helped engineers, researchers, and many others make significant progress with everything from voice/sound recognition to language translation and face recognition. It has also proved to be useful in the early detection of skin cancer and preventing blindness in diabetics.

If you sell your expertise for a living, you will discover significant benefits in transferring your knowledge to the world of Artificial Intelligence (AI). It is now possible to create an online chatbot with near human characteristics to deliver your intellectual property to the world. From your website your clients will be able to interact with the AI system to receive a personalised experience of the way you deliver your specialist skills. The Chatbot will be able to track the client's progress and adapt the learning experience to suit their individual mood, personality and abilities. Your unique talents will be instantly made available to a global audience 24 / 7. Up until August 2016 the software to build a chatbot has been in prototype with major corporations but now it's going mainstream.

If you are practitioner interested in deep learning for NLP, you may have different goals and requirements from the material. For example, you may want to focus on the methods and applications rather than the foundational theory. The course is comprised of 13 lectures, although the first and second lectures are both split into two parts. The complete lecture breakdown is provided below. The GitHub repository for the course provides links to slides, flash videos and reading for each lecture. I would recommend watching the videos via this unofficial YouTube playlist. Below is a course overview slide taken from the first lecture.

When we build these models, we always use a set of historical data to help our machine learning algorithms learn what is the relationship between a set of input features to a predicted output. We'll show how you can evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall, and present some solutions that can help when you encounter such scenarios. If we were to train a machine learning model and it learned to always predict an email as not spam (negative class), then it would be accurate 99% of the time despite never catching the positive class. Similarly, increasing the number of training examples can help in cases of high variance, helping the machine learning algorithm build a more generalizable model.