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Articles related to "learn"


Rejections, odd hours and the grind: The life of a new realtor is anything but easy

  • Kieraszewicz acquired his licence through online courses with the Ontario Real Estate Association.
  • A related key success factor is establishing a good connection with the client, said Mohamed Nada, a new realtor with Re/MAX LLC in Mississauga, Ont. Nada came by his interest in real estate from his father, who was also a realtor, and he’s been licensed since January.
  • Like Nada, she took on the rental deals that no one else in the brokerage wanted and was able to network as a hostess in order to acquire some clients.
  • Both Kieraszewicz and Sanca stress the importance of having a mentor available in order to learn the tricks of the trade that are not spelled out in the online courses, such as choosing a brokerage to work for.

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What Online Poker Players Can Teach Us About AI

  • Poker is considered a good challenge for AI, as it is seen as combination of mathematical/strategic play, and human intuition, especially about the strategies of others.
  • To see why let’s analyse how it plays against the Bart Simpson strategy: If your opponent always plays rock, you will play rock 1/3rd of the time, paper 1/3rd and scissors 1/3rd, meaning you will tie 1/3rd, win 1/3rd, and lose 1/3rd.
  • Many professional players are extremely good at bucketing their opponents into types and using different strategies against each “type”.
  • Specifically with online poker, this is almost purely based on patterns of play, so there is no theoretical reason why an AI can’t eventually become better than a human at it, and we can easily get 100s of millions of hands of data from professionals to try to mimic it.

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Teach Yourself Data Science in 10 Years — 3 Lessons from Peter Norvig (Director of Machine Learning at Google)

  • If you are interested in learning the fundamentals of data science, be prepared to invest the right amount of time and energy, that way you can master not just the superficial concepts, but the in-depth concepts of data science.
  • It took me 2 years of in-depth studies to master the basics of data science, and that is because I hold a PhD in physics, and I have a solid background in mathematics and programming.
  • Data science is a very multidisciplinary field that requires a solid background in advanced mathematics, statistics, programming, and other related skills in data analysis, data visualization, model building, machine learning, etc.
  • It took me 2 years of dedicated studies for me to master the fundamentals of data science, and that is because of my solid background in mathematics, physics and programming.

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What Online Poker Players Can Teach Us About AI

  • Poker is considered a good challenge for AI, as it is seen as combination of mathematical/strategic play, and human intuition, especially about the strategies of others.
  • To see why let’s analyse how it plays against the Bart Simpson strategy: If your opponent always plays rock, you will play rock 1/3rd of the time, paper 1/3rd and scissors 1/3rd, meaning you will tie 1/3rd, win 1/3rd, and lose 1/3rd.
  • Many professional players are extremely good at bucketing their opponents into types and using different strategies against each “type”.
  • Specifically with online poker, this is almost purely based on patterns of play, so there is no theoretical reason why an AI can’t eventually become better than a human at it, and we can easily get 100s of millions of hands of data from professionals to try to mimic it.

save | comments | report | share on


Teach Yourself Data Science in 10 Years — 3 Lessons from Peter Norvig (Director of Machine Learning at Google)

  • If you are interested in learning the fundamentals of data science, be prepared to invest the right amount of time and energy, that way you can master not just the superficial concepts, but the in-depth concepts of data science.
  • It took me 2 years of in-depth studies to master the basics of data science, and that is because I hold a PhD in physics, and I have a solid background in mathematics and programming.
  • Data science is a very multidisciplinary field that requires a solid background in advanced mathematics, statistics, programming, and other related skills in data analysis, data visualization, model building, machine learning, etc.
  • It took me 2 years of dedicated studies for me to master the fundamentals of data science, and that is because of my solid background in mathematics, physics and programming.

save | comments | report | share on


What Online Poker Players Can Teach Us About AI

  • Poker is considered a good challenge for AI, as it is seen as combination of mathematical/strategic play, and human intuition, especially about the strategies of others.
  • To see why let’s analyse how it plays against the Bart Simpson strategy: If your opponent always plays rock, you will play rock 1/3rd of the time, paper 1/3rd and scissors 1/3rd, meaning you will tie 1/3rd, win 1/3rd, and lose 1/3rd.
  • Many professional players are extremely good at bucketing their opponents into types and using different strategies against each “type”.
  • Specifically with online poker, this is almost purely based on patterns of play, so there is no theoretical reason why an AI can’t eventually become better than a human at it, and we can easily get 100s of millions of hands of data from professionals to try to mimic it.

save | comments | report | share on


Teach Yourself Data Science in 10 Years — 3 Lessons from Peter Norvig (Director of Machine Learning at Google)

  • If you are interested in learning the fundamentals of data science, be prepared to invest the right amount of time and energy, that way you can master not just the superficial concepts, but the in-depth concepts of data science.
  • It took me 2 years of in-depth studies to master the basics of data science, and that is because I hold a PhD in physics, and I have a solid background in mathematics and programming.
  • Data science is a very multidisciplinary field that requires a solid background in advanced mathematics, statistics, programming, and other related skills in data analysis, data visualization, model building, machine learning, etc.
  • It took me 2 years of dedicated studies for me to master the fundamentals of data science, and that is because of my solid background in mathematics, physics and programming.

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The Advantages of Peer Learning

  • Peer learning gives the learners considerably more practice than traditional teaching and learning methods in taking responsibility for their own learning and, more generally, learning how to learn.
  • After doing various reciprocal peer learning activities, they gave a debriefing questionnaire and the results were that 100% of the students increased their understanding of course content because they taught it and 97% agreed that it increased their retention of knowledge they taught to their peers.
  • Unlike some learning methods, like tests or exams, or high-pressure demonstrations of skills,  peer-to-peer learning creates a space where the learner can feel safe taking these risks without a sense that their boss is evaluating their performance while they are learning.
  • A secondary benefit of peer-to-peer learning is that the format itself helps employees develop management and leadership skills.

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How Machine Learning Improves Marketing Automation

  • AI applications like these are already available as products from third-party vendors, so digital marketing specialists don’t have to be data scientists or AI developers to use or build them.
  • However, it’s beneficial for executives and marketers to understand at least the foundations of machine learning to grasp the capabilities and develop strategies before making a considerable investment in high-value resources.
  • Regardless of how it’s deployed in your marketing technology stack, machine learning helps eliminate waste — wasted labor hours, wasted budget, wasted sales focus on low-value customers, and other draining endeavors.
  • She gave the example of the Associated Press (AP), whose staff of 65 writers could only meet 6 percent of the demand for quarterly earnings stories, so they began supplementing their articles with content drafted by an AI firm that automatically combed all corners of the internet for timely data and used natural language processing, a specialized type of machine learning, to generate news pieces.

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Review: Andrew Ng’s Machine Learning Course

  • Stanford’s Machine Learning course taught by Andrew Ng was released in 2011.
  • 8 years after publication, Andrew Ng’s course is still ranked as one of the top machine learning courses.
  • This has become a staple course of Coursera and, to be honest, in machine learning.
  • So let’s dive into my honest review of Andrew Ng’s Machine Learning course.
  • This 11-week completely online course is comprised of video and reading lectures, quizzes, and programming assignments.
  • The graded assignments are a great way to sift through the topic and understand the math going on with the models that are covered.
  • I am disappointed that it was not completed in a common machine learning language, but what you get out of it outweighs that want.
  • I highly recommend the paid version of this course to anyone who has just started their machine learning journey.

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