A better approach might be to observe and learn from the factory example, and adapt the following five lessons in order for knowledge workers to remain relevant in the coming new world of artificial intelligence. On one point, my colleagues are right: artificial intelligence will take over and automate standardized "knowledge work". This issue might be partially solved by training knowledge workers how to become "operators" of the artificial intelligence tools and understand artificial intelligence and machine learning. Some innovations will replace and disrupt traditional "knowledge work" completely.

Home-grown technology company Smartron and University of Southern California (USC) have collaborated to impart personalised education through the applications of machine learning and wearable technologies. USC's Center for Human Applied Reasoning and the Internet of Things (CHARIOT), along with Smartron, would focus on building the framework for creating an effective, classroom-based system for measuring the cognitive and affective influences on learning using smartphones and a range of sensors. "We are pleased to be the first Indian product brand to partner with USC's CHARIOT to collaborate on creating this new and hugely immersive and personalised learning programme based on IoT," said Mahesh Lingareddy, Founder, Smartron founder, on Friday. Smartron recently unveiled "tronX", a first-of-its-kind intelligent ecosystem that connects a range of devices, sensors and systems to "tronX" core, offering evolving experiences and services. "With our wearables powered by "tronX", we will be able to gather and analyse the data to help create the most effective learning interventions for students who may need it," Lingareddy added in a statement.

This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python. Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come. In the first section, I will show you how to use 1-D linear regression to prove that Moore's Law is true.

There is no doubt that Artificial Intelligence is one of the most sophisticated of all the emerging technologies. Artificial Intelligence deals with the understanding of machines and programming them to do tasks autonomously as well as helping them get smarter. It requires a great deal of understanding and problem-solving. It's also one of the most expensive subjects to learn if you are eager to secure a career in this field. Luckily for our lovely readers, we have compiled three courses for you below which will help you understand the basics of Artificial Intelligence for zero price.

Today is a tremulous time for a lot of people in almost every sector around the globe. Things are changing fast thanks primarily to awesome technological advances like nothing we have seen since the invention of gunpowder. No one can deny how the world has been and continues to be changed by gunpowder. Now it is the internet that is disrupting entire societies, both for good and for evil. Everything from Artificial Intelligence (AI) and Machine Learning (ML) to small IoT gadgets that monitor an employee's daily activities are blasting off into the stratosphere as they continue to develop.

About this course: Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist's toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated.

Lasso regression analysis is a shrinkage and variable selection method for linear regression models. The goal of lasso regression is to obtain the subset of predictors that minimizes prediction error for a quantitative response variable. The lasso does this by imposing a constraint on the model parameters that causes regression coefficients for some variables to shrink toward zero. Variables with a regression coefficient equal to zero after the shrinkage process are excluded from the model. Variables with non-zero regression coefficients variables are most strongly associated with the response variable.

Udacity's mission is to democratize education. Focused on self-empowerment through learning, Udacity is making innovative technologies such as self-driving cars available to a global community of aspiring technologists, while also enabling learners at all levels to skill up with essentials like programming, web and app development. If you love a challenge, and truly want to make a difference in the world, read on! A Student Experience Lead is a educational strategist and program manager in one, with a strong pedagogical sense. You should take pride in ensuring that the students in your Nanodegree program receive the best possible learning experience.

Mike Salem from Udacity's Robotics Nanodegree is hosting a series of interviews with professional roboticists as part of their free online material. Chris is a former Wired magazine editor turned robotics company co-founder and CEO. You can find all the interviews here. We'll be posting them regularly on Robohub.

No matter where you are in your coding journey this course will get you up and running with Apache Spark, from installation and configuration to power user with 5.5 hours of top quality video tutorials. The first chapters are a step by step guide through the fundamentals of Spark programming, covering data frames, aggregations and data sets. Next you'll dive into what you can do with all the data you collect using Spark, filter results with R and expose your data to Python for deeper processing and presentation using charts and graphs. After that, you go further into the capabilities of Spark's stream processing, machine learning, and graph processing libraries. The last chapter combines all the skills you learned from the preceding chapters to develop a real-world Spark application.By the end of this video, you will be able to consolidate data processing, stream processing, machine learning, and graph processing into one unified and highly interoperable framework with a uniform API using Scala or Python.