Across the world, higher education is increasingly being judged through the lens of employability. More and more, politicians are asking universities how they are preparing students for work, and even tying their funding to their graduates' success in the workplace. In the West, this has mainly been a result of the squeeze on the public purse and – in some countries, at least – an accompanying rise in tuition fees. But there is also growing anxiety about the technological revolution's potential to replace large numbers of human workers with computers and robots if humans can't keep one step ahead in the race to acquire skills. So how well are universities meeting the challenge of preparing graduates for the digital age?
Work as we know it is in a state of flux. Technology is imposing rapid change, and the rise in automation capabilities and artificial intelligence are the chief catalysts. As Salesforce's Futurist, I spend a lot of time forward-thinking and analysing trend data, and have shared my thoughts on what this technological change means for the future of work and how to navigate it. There's a lot of angst in the world right now that the rise of smart technologies are going to disemploy vast numbers of people. I appreciate why there's anxiety, but if we look at history as a predictor of the future, this simplistic idea that'technology steals jobs' is unfounded.
Thank you all for the huge response to this emerging course! We are delighted to have over 300 students in over 145 different countries. I'm genuinely touched by the overwhelmingly positive and thoughtful reviews. It's such a privilege to share and introduce this important topic with everyday people in a clear and understandable way. I'm also excited to announce that I have created real closed captions for all course material, so weather you need them due to a hearing impairment, or find it easier to follow long (great for ESL students!)... I've got you covered.
Our world is advancing at an extremely rapid rate. Technologies such as artificial intelligence, machine learning, drones, internet of things, augmented reality, and blockchain are growing in popularity every single day. Personally, I feel another industrial revolution is approaching quickly and the world we will in is going to drastically change. Blockchain is a difficult technology to understand but it has the potential to impact many organizations across the globe. If you're looking to get a head start on an innovative idea that will change our world then you're in the right place!
Stanford researchers have developed an algorithm that offers diagnoses based off chest X-ray images. A paper about the algorithm, called CheXNet, was published Nov. 14 on the open-access, scientific preprint website arXiv. "Interpreting X-ray images to diagnose pathologies like pneumonia is very challenging, and we know that there's a lot of variability in the diagnoses radiologists arrive at," said Pranav Rajpurkar, a graduate student in the Machine Learning Group at Stanford and co-lead author of the paper. "We became interested in developing machine learning algorithms that could learn from hundreds of thousands of chest X-ray diagnoses and make accurate diagnoses." The work uses a public data set initially released by the National Institutes of Health Clinical Center on Sept. 26.
Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time we dive deep into Machine Learning.
Who would have thought that the stories around self-driven cars could actually come true, so much so that machine learning algorithms can enable computers to communicate with humans, drive cars, play games and do things human cannot do. Machine Learning with its mathematical algorithms and scientific innovations have become a huge part of our lives. For example, when Google auto-corrects a misspelled word, it applies probability algorithm, an action performed using Machine Learning, which compares the database of the previous searches done by millions of other users and predicts the word we intend to use. With the ever-increasing knowledge in science and technology, machine learning is not far behind to be the new switchboard for Higher Education, personalising education at all levels. It reads and identifies the data patterns to inform algorithms that can make data-driven predictions and decisions.
Note: Machine Learning typically data analyst are some of the most expensive and coveted professionals around today.Data analysts enjoy one of the top-paying jobs, with an average salary of $140,000 according to Glassdoor .That's just the average! Machine Learning is very important in Data mining. Also,machine Learning is a growing field. Our course is designed to make it easy for everyone to master machine learning. This amazing Course will help you quickly master all the difficult concepts and will the learning will be a breeze.
I'm practicing explaining ML concepts, so, experts, please correct me if any of my points are incorrect or misleading. For example, I was reading an example of regression analysis where the factors such as cylinders, displacement, horsepower affect the mpg of a car. The topic would touch on how many of these factors, or neurons is too much. It seems like you may be conflating features, data about what you're observing that you give as inputs to your model, such as "cylinders, displacement, horsepower", and neurons, which are fine-tuned by training to make up the function you're trying to learn. Maybe I can help you by giving a few reasons people don't just keep increasing the number of neurons.
For Peter Cao, who has dedicated 16 years of his career to teaching chemistry in a high school in central China's Anhui province, in every teacher there lives a "doctor". He spends two to three hours a day grading assignments, a process the 38-year-old describes as "diagnosing". "By reviewing the homework of my pupils, I can have an overall picture about their understanding of the lessons I give," Cao said, adding that this "diagnosis" helps him draw up a teaching plan for the following day. But if the Chinese online education start-up Master Learner has its way, Cao and his 14 million fellow teachers in China will be able to hand this time-consuming review process to a "super teacher", a powerful "brain" capable of answering nearly 500 million of the most tested questions in China's middle schools as well as scoring high points in each Gaokao test, China's life-changing college entrance exam, for the past 30 years. If the super teacher sounds too smart to be human, that is because it is not.