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: Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting.

Nothing beats a traditional good book about a subject, and even though it may be old school compared to the other tips I will provide after, there is something to be said for the amount of time and level of detail to goes into the writing of a book on a subject. In my opinion, a book does not only provide the deepest knowledge laid out on subject, but owning a physical object to surround yourself with also helps you stay inspired on your quest to learn a new skill. Here are a few that I recommend. Considered an esssential book to start machine learning. A lot of data scientists recommend this one, and many claim to have read it multiple times.

With consumers screaming and queuing up for the next big thing, the Companies are anxious to tap the right talent of software developers with the required skill sets. The next generation of software developers will have to be more than coders--they will have to be intuitive problem solvers who can see the big picture, who recognize that the landscape is constantly changing, and who realize it is their responsibility keep up. We have curated a summary of top IoT skills needed in today's developer ecosystem. If you are looking to pursue a career in IoT, read through them and get yourself a headstart. Building an IoT system requires a team effort.

I asked the coder on the project how he trained his model, and he said he didn't build a model, just used the facial recognition library in OpenCV. Because there are a new level of resources available, you can go from concept to finished product in a few days -- and if you are thinking even bigger, with the pieces as products not process, you can use that ease of process to create on a much larger magnitude scale. Efficiency of Intoxication: The TikiTron uses classic cocktail recipes, and carefully calibrated pumps to deliver consistently excellent cocktails -- if I do say-so myself (I wrote the code.) In short, it looked like a science experiment in process, not a polished bot.

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!