Automation, robotics, algorithms and artificial intelligence (AI) in recent times have shown they can do equal or sometimes even better work than humans who are dermatologists, insurance claims adjusters, lawyers, seismic testers in oil fields, sports journalists and financial reporters, crew members on guided-missile destroyers, hiring managers, psychological testers, retail salespeople, and border patrol agents. A recent study by labor economists found that "one more robot per thousand workers reduces the employment to population ratio by about 0.18-0.34 When Pew Research Center and Elon University's Imagining the Internet Center asked experts in 2014 whether AI and robotics would create more jobs than they would destroy, the verdict was evenly split: 48% of the respondents envisioned a future where more jobs are lost than created, while 52% said more jobs would be created than lost. This survey noted that employment is much higher among jobs that require an average or above-average level of preparation (including education, experience and job training); average or above-average interpersonal, management and communication skills; and higher levels of analytical skills, such as critical thinking and computer skills. A focus on nurturing unique human skills that artificial intelligence (AI) and machines seem unable to replicate: Many of these experts discussed in their responses the human talents they believe machines and automation may not be able to duplicate, noting that these should be the skills developed and nurtured by education and training programs to prepare people to work successfully alongside AI.
"It's now popping into almost every piece of software," said Josh Bersin, principal and founder of consulting firm Bersin by Deloitte. Its main AI and HR analytics product is Cornerstone Insights, what CTO Mark Goldin called "machine learning in a box." The dispassionate analysis that AI brought to Expedia's recruiting practices can also be applied to performance management, which Holger Mueller, vice president and principal analyst at Constellation Research, considers talent management's core function -- and the part that's most broken. "The applications of AI basically are analytics applications, where the software is using history and algorithms and data to be smarter and smarter over time," Bersin explained.
Our curriculum prepares us for a lifetime career, but a child today can expect to change jobs at least seven times over the course of their lives – and five of those jobs don't exist yet. We have already moved to a data driven economy in the Fourth Industrial Revolution, and this is going to increase even more in the coming years and Machine Learning (ML) and Artificial Intelligence (AI) become business as usual in every aspect of our lives. This can help them to gain knowledge in data analysis at the college level where they will have to learn some kind of a programming language like MATLAB which allows implementation of algorithms. While AI and ML are disrupting jobs, there is great potential to use AI in education.
ML is soon becoming the de facto technology for every product and its Product Manager, ML offers only two choices (1) Embrace ML effectively or (2) Fad into oblivion. PMs should focus on identifying the problem and it is entirely the responsibility of engineering to identify right flavors of ML based on the problem statement provided by PM. After Andrew NG course, I explored for few courses in Udemy for hands-on experience exploring ML algorithms and I picked the following course: Python for Data Science and Machine Learning Bootcamp (it is cheaper:-)). I completed 50% of the course and the focus is exclusively on using existing Python libraries for solving ML problems.
About this course: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course.
In a recent Quora session, Kaggle CTO Ben Hamner outlined his advice to study machine learning. At the end of the post, I suggest an alternative platform, Startcrowd, to build real-world AI products, instead of statistical models. If you start from scratch, with no coding skills, nor data science experience, I personally recommend the Python course on Codecademy, the Andrew Ng ML course on Coursera, the Intro to Data Science on Udacity, and the Stanford courses on Convolutional Neural Networks and NLP. If performance is really the issue, then you can follow Ben's third advice: acquire more data, improve data cleaning, or optimize the model like a Kaggle player.
New and innovative technologies enable a variety of instructional environments that help students overcome many traditional boundaries and constraints to learning. As the classroom becomes more of an abstraction than a physical space, educators and learners embrace a variety of pioneering tech-powered teaching and learning paradigms that will serve students well upon graduation.
Training strategies have long since stopped being considered as'nice to have' motivational activity and more and more organisations are expecting close alignment of training and business in order to make training strategies effective. Some of the key expectations of the business from training include outcome driven approach, velocity in training delivery, adaptation to the dynamic needs of the business and tuning to the millennial mindsets in the design of the programme. The HR Information Systems, Performance Management system and the Learning Management Systems should be integrated and provide the bedrock system for talent development for the organisation. In order to support the learning requirements of the employees, the digital learning platform should not only tap into the knowledge and experience of experts in the organisation, but should also recognise the potential of Artificial Intelligence (AI) and machine learning as well.
In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
I did the coursera course and did CS231n: Convolutional Neural Networks for Visual Recognition, read up on basic theory, did some image processing networks like VGG, Resnets and most recently trying to get Faster-RCNN to work, so my currently knowledge is ML basics and heavily focussed on ML in the Image domain. I recently landed my first ML job at a company that does mostly NLP, so I lack a lot of knowledge in that domain. I'm currently reading the NLTK book, which has been very approachable in introducing basic concepts in a code-focussed way. So I was wondering if anyone could point me to some good mid to advanced level resources (online courses/videos/books) to get up to speed with where the field is at now, to help me understand current research and more advanced concepts?