About this course: Welcome to the art and science of machine learning. In this course you will learn the essential skills of ML intuition, good judgment and experimentation to finely tune and optimize your ML models for the best performance. In this course you will learn the many knobs and levers involved in training a model. You will first manually adjust them to see their effects on model performance. Once familiar with the knobs and levers, otherwise known as hyperparameters, you will learn how to tune them in an automatic way using Cloud Machine Learning Engine on Google Cloud Platform.

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

China is on the verge of a robotics revolution. In 2014 it has the lowest robot to worker ratio compared to that of its peers – South Korea, Germany and the United States. Today, that is changing as China is pivoting towards robotics. The Chinese government has laid out a 5-year plan that included robotics to steer economic and social development. It is estimated that the number of industrial robots in China has been growing at an unprecedented rate of 40% per year as the government plans to triple its number to 100,000 by 2020 to promote high-end manufacturing.

About this course: This course will teach you how to perform data analysis using MongoDB's powerful Aggregation Framework. You'll begin this course by building a foundation of essential aggregation knowledge. By understanding these features of the Aggregation Framework you will learn how to ask complex questions of your data. This will lay the groundwork for the remainder of the course where you'll dive deep and learn about schema design, relational data migrations, and machine learning with MongoDB. By the end of this course you'll understand how to best use MongoDB and its Aggregation Framework in your own data science workflow.

Over the last few years, Google and Coursera have regularly teamed up to launch a number of online courses for developers and IT pros. Among those was the Machine Learning Crash course, which provides developers with an introduction to machine learning. Now, building on that, the two companies are launching a machine learning specialization on Coursera. This new specialization, which consists of five courses, has an even more practical focus. The new specialization, called "Machine Learning with TensorFlow on Google Cloud Platform," has students build real-world machine learning models. It takes them from setting up their environment to learning how to create and sanitize datasets to writing distributed models in TensorFlow, improving the accuracy of those models and tuning them to find the right parameters.

Let's get started with knowing what exactly a neural network is- A neural network is an abstract concept which is derived from the neurons from our brain. So, before diving further into details let's step back and take a look at what the neurons in our brain do and how do the artificial neural network resemble the natural neurons in our brain. But wait a second, why are we even doing this? Why do we need a neural network? We have come far as a species but we still face a lot of issues which are yet to be solved.

Pupils have class equipped with electronic devices. The comprehensive report on Internet-based education in China discussed practices and development trends in online education, both in schools and for self-motivated learners in society. It said the overall Internet-based learning index was 3.61 out of five, with online learners scoring lower in metrics measuring their information literacy. It also showed teachers are calling for better equipment and services to support the integration of IT with education. The report mentioned more efforts are needed to make online education universal and accessible through training and publicity.

During her freshman year, Stephanie Tena, a 16-year-old programmer, was searching the internet for coding programs and came across a website for an organization called AI4All, which runs an artificial-intelligence summer camp for high-schoolers. On the site, a group of girls her age were gathered around an autonomous car in front of the iconic arches of Stanford's campus. "AI will change the world," the text read. Tena thought maybe she could. She lives in a trailer park in California's Salinas Valley; her mom, a Mexican immigrant from Michoacán, picks strawberries in the nearby fields.*

This animation shown to school children asks, which robot goes missing? You can learn an important lesson about artificial intelligence from the GIF above. The picture starts off with 17 robots, yet when the pieces have finished moving around, it shows just 16. Where is the missing robot?* Paul Curzon thinks you should. The professor of computer science at Queen Mary, University of London says that Silicon Valley has become so good at making technology invisible and "just work" as Steve Jobs used to quip, that it can seem like magic. That could become a problem for kids, the next generation of technology users.

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for IT professionals and data-scientists. The scikit-learn library is one of the most popular platforms for everyday Machine Learning and data science because it is built upon Python, a fully featured programming language. This comprehensive 3-in-1 course is your one-stop solution to everything that matters in mastering machine learning algorithms and their implementation. Develop pipelines and process data through manipulation, extraction, and data-cleansing techniques. Learn clean coding techniques which are applicable to any scalable Machine Learning projects.