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Machine learning skills for software engineers

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Even the best researchers couldn't crack hard problems like image recognition in the real world. And an increasing number of developers are beginning to work on a variety of different, serious machine learning projects as they recognize that machine learning and even deep learning have become more accessible. Developers are beginning to fill roles as data engineers in a "data ops" style of work, where data-focused skills (data engineering, architect, data scientist) are combined with a devops approach to build things such as machine learning systems. Pretty much the most basic skill in building machine learning systems is the ability to look at the history of decisions that two models have made and determine which model is better for your situation.


Machine learning skills for software engineers

#artificialintelligence

Even the best researchers couldn't crack hard problems like image recognition in the real world. And an increasing number of developers are beginning to work on a variety of different, serious machine learning projects as they recognize that machine learning and even deep learning have become more accessible. Developers are beginning to fill roles as data engineers in a "data ops" style of work, where data-focused skills (data engineering, architect, data scientist) are combined with a devops approach to build things such as machine learning systems. Pretty much the most basic skill in building machine learning systems is the ability to look at the history of decisions that two models have made and determine which model is better for your situation.


How Are We Preparing Students for the Artificial Intelligence New Normal?

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As I gaze into the palantir afforded me as a co-host of "The EdTech Situation Room" each week with Jason Neiffer (@techsavvyteach), this is part of the future I see for our students, our society, and ourselves in the coming decades. This 98 second video, which I titled "EdTech Situation Room Promo Trailer," is the result of my thinking about this question tonight. This question of what an emerging "artificial intelligence first" rather than "mobile first" worldview (which Google announced at Google IO 2017) should mean for schools is something I discussed on The EdTech Situation Room back on May 17, 2017, with Jason Neiffer (@techsavvyteach) and Ben Wilkoff (@bhwilkoff). Check out the "Narrated Slideshow – Screencast" and "Digital Storytelling" pages of ShowWithMedia.com for additional resources and examples related to these media project types.


If you need someone to fly a drone, call a football manager

Los Angeles Times

Eric Sondheimer has been covering high school sports for the Los Angeles Times since 1997 and in Southern California since 1976. It's the hottest new toy in high school football: drones flying above practice fields filming practices. It's only a matter of time before someone has a drone football manager competition to see who can be the next F-16 pilot using remote control. At Loyola High, junior football manager Gabriel Danaj received a $1,500 drone from his grandmother as a gift and offered to use it to film Loyola practices.


Solving The Machine Learning Skills Gap Articles Big Data

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Machine learning is, as you would likely imagine, extremely complicated, and not something your run-of-the mill computer engineer is going to be capable of without proper training. It requires someone with a background in computer science, likely with a doctorate in the sciences, as well as a significant amount of practical experience working with data at scale. Given that there is already a dearth of qualified data scientists, there is little to suggest that the situation is going to be any different when it comes to machine learning. Even The US Government has expressed concerns about the lack of AI talent.


Solving The Machine Learning Skills Gap 7wData

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Of these job postings, 61% in the AI industry were for machine learning engineers, 10% were for data scientists and just 3% were for software developers. According to a survey from Tech Pro Research, just 28% of companies have some experience with AI or machine learning, and more than 40% said their enterprise IT personnel don't have the skills required to implement and support AI and machine learning. Given that there is already a dearth of qualified data scientists, there is little to suggest that the situation is going to be any different when it comes to machine learning. Most companies undertaking machine learning projects already own and store vast quantities of data, but few enterprises have such copious quantities of data, and even then it is often siloed and requires aggregating, which is a lengthy and difficult process that few are resourced for.


Meet the 13-year-old prodigy taking IBM and artificial intelligence by storm - Watson

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Read the full ABC article and watch the video interview to learn more about Tanmay and his work in the field of AI. The Australian Broadcasting Corporation (ABC) recently profiled 13-year-old Canadian tech prodigy Tanmay Bakshi who started using computers at age five, launched his first app at age nine, and has been working with IBM's AI and cognitive APIs for a couple of years now. ABC notes: "the Canadian teen has become a global force in programming and commands more than 20,000 subscribers on his YouTube channel that teaches computer coding." You can also watch Tanmay's video, "IBM Watson, Machine Learning: How to use the "Retrieve and Rank" service in IBM Bluemix", one of 80 tutorials he has created and made available on the "Tanmay Teaches" YouTube channel.


Machine Learning Skills Among Data Scientists

@machinelearnbot

Data scientists have a variety of different skills that they bring to bear on Big Data projects. While machine learning is a hot skill to possess, a recent study by Evans Data Corp. found that about a third of developers (36%) who are working on Big Data projects employ elements of machine learning. In today's post, I wanted to explore how machine learning skill proficiency varied across different types of data professionals. Specifically, we asked them to indicate their level of proficiency across 25 different data skills (including machine learning), satisfaction with work outcomes of analytics projects and their job role.


Drone drops water balloons at Division 1 track prelims

Los Angeles Times

Eric Sondheimer has been covering high school sports for the Los Angeles Times since 1997 and in Southern California since 1976. The Southern Section Division 1 track and field preliminary meet at Trubuco Hills High School on Saturday featured a water balloon attack from a lone drone. Near the start of the meet, around 11:30 a.m., a group of people positioned themselves on the hill above the track and allegedly flew a drone carrying water balloons over the track. Ayres sent out his kids to find the group of people commanding the drone on Friday evening and CIF officials supposedly notified the Orange County Sheriff's Department about the incident Saturday morning.


Developing Machine Learning Skills on the Job - DATAVERSITY

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Data Scientists use Machine Learning (ML) skills to develop powerful algorithms to make sense of the avalanche of data. Moreover, when some of these Data Scientists plan on specializing in Machine Learning science or engineering, they struggle even further to adapt and hone their generic knowledge into the more specialized areas of ML. On the other hand, a bright computer scientist or engineer with some exposure to Machine Learning can surely hope to pursue a career in ML Engineering. The ML competition sites provide an excellent opportunity to bright Data Scientists to describe, present, and solve a particular problem through ML techniques.