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 competitive organizations. Machine Learning applications are everywhere, from self-driving cars, spam detection, document searches, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and data science. The main challenge is how to transform data into actionable knowledge. In this course you will learn all the important Machine Learning algorithms that are commonly used in the field of data science.

Self driving cars thought to be a distant dream just a few decades ago. However, thanks to the recent progress made in various fields of computer science, this dream is becoming a reality now. Computer vision plays a central role in understanding the capabilities these vehicles required to be able to operate not only under standard conditions, but also under the most unexpected situations. Machine Learning is everywhere these days. We live in a world where Machine Learning and Artificial Intelligence is not obscure mathematical and science fiction anymore they have become crucial part of our lives.

Apple has expanded its office space at Two Union Square, a 56-story office tower in downtown Seattle, according to GeekWire. The report claims Apple will soon occupy five floors inside the skyscraper, up from a floor-and-a-half of space it originally leased, based on permits filed with the city and the publication's own visits to the building. The expansion gives Apple more than 70,000 square feet of space, enough for potentially close to 500 people, the report adds. Last year, Apple confirmed that its Seattle engineering hub is focused on artificial intelligence and machine learning, technologies that power the likes of Siri and advanced facial recognition on the iPhone X. We're trying to find the best people who are excited about AI and machine learning -- excited about research and thinking long term but also bringing those ideas into products that impact and delight our customers.

If you want to bring up a kid to be a successful investor or entrepreneur, the current education system says they should be studying STEM subjects, cramming facts and figures, and immersing themselves in coding class. I've spent my working life as an entrepreneur and investor – I've founded startups, and now invest across Europe for GV (the venture capital investment arm of Google) – but as a father, when I look at the way we're educating our kids, I think there's something missing. Machines are already superintelligent on many axes, including memory and processing speed. Unfortunately, those are the attributes our education system currently rewards, with an emphasis on learning by rote. Part of my job as an investor is to attempt to predict the future – I need to make bets on the way we'll be behaving in the next two, five, ten and 20 years. Computers already store facts faster and better than we do, but struggle to perfect things we learn as toddlers, such as dexterity and walking.

In recent years, technology has shaped classrooms all over the world. Not too long ago, chalk and blackboards were all you needed, but then computers, tablets, and the internet came along. But recently, education is being augmented and taken to next level by virtual reality (VR) and artificial intelligence (AI). According to a recent Pearson report, AI is set to positively transform education in the coming years. "The future offers the potential of even greater tools and supports. Imagine lifelong learning companions powered by AI that can accompany and support individual learners throughout their studies -- in and beyond school -- or new forms of assessment that measure learning while it is taking place, shaping the learning experience in real time," the authors of the report wrote.

TensorFlow is quickly becoming the technology of choice for deep learning, because of how easy TF makes it to build powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction. This is a comprehensive, from-the-basics course on TensorFlow and building neural networks. It assumes no prior knowledge of Tensorflow, all you need to know is basic Python programming.

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

Artificial Intelligence, increasingly relevant in the modern world where everything is driven by technology and data, is the process of automating any system or process to carry out complex tasks and functions automatically, in order to achieve optimal productivity. This video explains the basics of AI using popular Java-based libraries and frameworks to build your smart applications. We will cover easy-to-complex artificial intelligence tasks such as genetic programming, heuristic searches, reinforcement learning, neural networks, and segmentation with the practical approach we mentioned earlier. By the end of this video, you will have a solid understanding of Artificial Intelligence concepts. You will be able to build your own smart applications for multiple domains, as required.

We're living the middle of a golden data age where our best and brightest are leveraging Artificial Intelligence technologies to create self-driving cars, facial recognition tools, and smart assistants that straddle the line between science and science fiction. Now, these breakthroughs are only the beginning, which is why it pays to understand how they work now, so you can help pioneer the next wave of AI innovations. The Pay What You Want: AI & Deep Learning Bundle can kickstart your deep dive into this field, and it's available for a price you pick. Here's how the deal works: Simply pay what you want, and you'll instantly unlock one of the collection's e-learning resources. Beat the average price paid, and you'll get the remaining nine at no extra charge.

About this course: Spatial (map) is considered as a core infrastructure of modern IT world, which is substantiated by business transactions of major IT companies such as Apple, Google, Microsoft, Amazon, Intel, and Uber, and even motor companies such as Audi, BMW, and Mercedes. Consequently, they are bound to hire more and more spatial data scientists. Based on such business trend, this course is designed to present a firm understanding of spatial data science to the learners, who would have a basic knowledge of data science and data analysis, and eventually to make their expertise differentiated from other nominal data scientists and data analysts. Additionally, this course could make learners realize the value of spatial big data and the power of open source software's to deal with spatial data science problems. This course will start with defining spatial data science and answering why spatial is special from three different perspectives - business, technology, and data in the first week.