About this course: In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.

About this course: This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team or working individually within a community of developers, this course will give you the knowledge of R needed to make useful contributions in those settings. As the first course in the Specialization, the course provides the essential foundation of R needed for the following courses. We cover basic R concepts and language fundamentals, key concepts like tidy data and related "tidyverse" tools, processing and manipulation of complex and large datasets, handling textual data, and basic data science tasks. Upon completing this course, learners will have fluency at the R console and will be able to create tidy datasets from a wide range of possible data sources.

About this course: Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. This course is the first in a sequence of three.

About this course: In previous courses in the Specialization, we have discussed how to sequence and compare genomes. This course will cover advanced topics in finding mutations lurking within DNA and proteins. In the first half of the course, we would like to ask how an individual's genome differs from the "reference genome" of the species. Our goal is to take small fragments of DNA from the individual and "map" them to the reference genome. We will see that the combinatorial pattern matching algorithms solving this problem are elegant and extremely efficient, requiring a surprisingly small amount of runtime and memory.

About this course: This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models. OBJECTIVES This course teaches participants the following skills: Identify use cases for machine learning Build an ML model using TensorFlow Build scalable, deployable ML models using Cloud ML Know the importance of preprocessing and combining features Incorporate advanced ML concepts into their models Productionize trained ML models PREREQUISITES To get the most of out of this course, participants should have: Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python Familiarity with Machine Learning and/or statistics Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google is currently blocked in China).

About this course: 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.

About this course: By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area The Internet of Things (IoT) is an area of rapid growth and opportunity. Technical innovations in networks, sensors and applications, coupled with the advent of'smart machines' have resulted in a huge diversity of devices generating all kinds of structured and unstructured data that needs to be processed somewhere. Collecting and understanding that data, combining it with other sources of information and putting it to good use can be achieved by using connectivity, analytical and cognitive services now available on the cloud, allowing development and deployment of solutions to be achieved faster and more efficiently than ever before. This course is an entry level introduction to developing and deploying solutions for the Internet of Things.

The AI (artificial intelligence) revolution is well and truly upon us, and we are at a significant watershed moment in our lives where AI could become the new electricity – pervasive and touching every aspect of our life. While many industries including healthcare, education, retail and banks have already started adopting AI in key business aspects, there are also new business models which are predicated on AI. With the global market of AI expected to grow at 36% annually, reaching a valuation of $3 trillion by 2025 from $126 bn in 2015, new age disruption is not only redefining the way traditional businesses are run, but is also unfolding as a new'factor of production'. However, the fear of what might happen once AI evolves into artificial general intelligence – which can perform any intellectual task that a human can do – has now taken centre stage with the ongoing debate between two tech titans – Elon Musk and Mark Zuckerberg. Similarly, Microsoft co-founder Bill Gates had also voiced his views that in a few years, AI would have evolved enough to warrant wide attention, while Facebook has ended up shutting down one of its AI projects as chatbots had developed their own language (unintelligible to humans) to communicate.

About this course: This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn how to carry out no-ops data warehousing, analysis and pipeline processing. Prerequisites: • Google Cloud Platform Big Data and Machine Learning Fundamentals • Experience using a SQL-like query language to analyze data • Knowledge of either Python or Java Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google is currently blocked in China).

Operating a data science team is not something that can just be learned by watching lectures and videos on Coursera and Udemy. Don't get us wrong, they are great places to learn data science and machine learning theory with practice problems. However, they don't teach good business practices, and how to operate a data team in a business settings. Knowing algorithms, and how to use Hadoop is not enough to have an effective data team. Teams have to work with other departments, they have to maintain software, report to executives, and of course, return business value!