This course will explain how to use scikit-learn to do advanced machine learning. If you are aiming to work as a professional data scientist, you need to master scikit-learn! It is expected that you have some familiarity with statistics, and python programming. It's not necessary to be an expert, but you should be able to understand what is a Gaussian distribution, code loops and functions in Python, and know the basics of a maximum likelihood estimator. The course will be entirely focused on the python implementation, and the math behind it will be omitted as much as possible.

Over the years, Andrew Ng has worn many hats - Coursera co-founder, former Baidu chief scientist, founding lead of Google Brain team, and Stanford University adjunct professor. But lately, he has emerged as the leading influencer championing artificial intelligence (AI). Well over 1.5 million people have enrolled in his AI courses in Coursera. In a chat with Vinod Mahanta, Ng talks about recent AI controversies: Elon Musk versus Mark Zuckerberg spat on dangers of AI, Facebook AI chatbots creating their own language and job displacements. Edited excerpts: In an experiment recently, Facebook chatbots created their own language and had to be shut down.

Science, technology, engineering, and math (STEM) fields play growing roles in national and international economies by driving innovation and generating high salary jobs. Yet, the US is lagging behind other highly industrialized nations in terms of STEM education and training. Furthermore, many economic forecasts predict a rising shortage of domestic STEM-trained professions in the US for years to come. One potential solution to this deficit is to decrease the rates at which students leave STEM-related fields in higher education, as currently over half of all students intending to graduate with a STEM degree eventually attrite. However, little quantitative research at scale has looked at causes of STEM attrition, let alone the use of machine learning to examine how well this phenomenon can be predicted. In this paper, we detail our efforts to model and predict dropout from STEM fields using one of the largest known datasets used for research on students at a traditional campus setting. Our results suggest that attrition from STEM fields can be accurately predicted with data that is routinely collected at universities using only information on students' first academic year. We also propose a method to model student STEM intentions for each academic term to better understand the timing of STEM attrition events. We believe these results show great promise in using machine learning to improve STEM retention in traditional and non-traditional campus settings.

Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I'll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn't. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon.

Andrew Ng taught one of the most-viewed online courses of all time--more than 1.5 million people have registered to take one of the many sequences of his free online course about machine learning. That experience spurred him to co-found Coursera. Today Ng announced that this summer he's launching sequels to that blockbuster, with a series of courses on the AI concept known as deep learning. For the past two years Ng had been applying concepts of deep learning in the commercial sector, as a chief scientist for the Chinese tech giant Baidu. But he left that company in March, and since then has been working on three undisclosed projects in AI.

Machine learning is increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this course, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the course, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

Mike Salem from Udacity's Robotics Nanodegree is hosting a series of interviews with professional roboticists as part of their free online material. Jillian is a video game designer turned roboticist attempting to combine games in robotics in a unique user experience. You can find all the interviews here. We'll be posting them regularly on Robohub.

Data science and machine learning are some of the top buzzwords in the technical world today. Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. Python is one of the most popular languages used for machine learning and arguably, the best entry point to the fascinating world of machine learning (ML). If you're interested to explore both the programming and machine learning world with python, then go for this course.

R is considered as lingua franca of Data Science. Candidates with expertise in R programming language are in exceedingly high demand and paid lucratively in Data Science. IEEE has repeatedly ranked R as one of the top and most popular Programming Languages. Almost every Data Science and Machine Learning job posted globally mentions the requirement for R language proficiency. All the top ranked universities like MIT have included R in their respective Data Science courses curriculum.

What does the future of work look like? Will there still be jobs even if the nature of work is exceptionally different from today? New technologies undoubtedly changed the way we work. Recent studies suggest that unemployment rate today is significant in most developed nations and it's only going to get worse. By 2030, mid-level jobs will be by and large obsolete.