Distance Learning

How to become a data scientist from scratch - Udemy


Get your team access to Udemy's top 2,000 courses anytime, anywhere. So you want to become a data scientist hm? If your answer is: Yes that's correct, then you are at the right place to start! Data science is the most interesting topic in the world we live in and beside that also highly rewarding. Any kind of machine learning (self driving cars, stock market prediction, image recognition, text analyzing or simply getting insights of huge datasets - it's all part of data science.



Just to let you know, if you buy something featured here, Mashable might earn an affiliate commission. It's time to give that ol' brain of yours a thorough dusting-off, so to speak: Through the Full Neuro-Linguistic Programming (NLP) Diploma Course, you might be able to hack your way to an improved way of thinking. NLP, in case you haven't heard, is a set of rules and techniques designed to help you shape your personal psychology and achieve self-actualization. Some see it as pseudo-science, while others swear by it. Either way, the idea is that you can train your brain to eliminate phobias, tweak bad habits, and even gain a deeper understanding of others' body language -- skills that are beneficial in the workplace, your social life, and beyond.

which is the best book for python machine learning ? • r/Python


I would recommend that you start with Introduction to Statistical Learning with R (usually shortened as ISLR). A lot of people have adapted the examples to Python if you google a bit and it's an excellent book that hides just enough complexity to not be overwhelming. Plus, once you have a good understanding of all of it, you can either graduate to the more extensive version (Elements of Statistical Learning, usually shortened as ESL) for a more rigorous treatment of the same thing, or choose to go for something different like Bishop's Pattern Recognition and Machine Learning. ISLR is free as a pdf and has a corresponding MOOC. ESL doesn't, but is also free on the author's website.

Online learning: Machine learning's secret for big data


In the field of machine learning, online learning refers to the collection of machine learning methods that learn from a sequence of data provided over time. In online learning, models update continuously as each data point arrives. You often hear online learning described as analyzing "data in motion," because it treats data as a running stream and it learns as the stream flows. Classical offline learning (batch learning) treats data as a static pool, assuming that all data is available at the time of training. Given a dataset, offline learning produces only one final model, with all the data considered simultaneously.

China wants to bring #artificialintelligence to its classrooms to boost its education system: "super teacher" is an AI powered education platform developed by online education start-up Master Learner's 300 engineers • r/Sino


For Peter Cao, who has dedicated 16 years of his career to teaching chemistry in a high school in central China's Anhui province, in every teacher there lives a "doctor". He spends two to three hours a day grading assignments, a process the 38-year-old describes as "diagnosing". "By reviewing the homework of my pupils, I can have an overall picture about their understanding of the lessons I give," Cao said, adding that this "diagnosis" helps him draw up a teaching plan for the following day. But if the Chinese online education start-up Master Learner has its way, Cao and his 14 million fellow teachers in China will be able to hand this time-consuming review process to a "super teacher", a powerful "brain" capable of answering nearly 500 million of the most tested questions in China's middle schools as well as scoring high points in each Gaokao test, China's life-changing college entrance exam, for the past 30 years. If the super teacher sounds too smart to be human, that is because it is not.

Dive into Deep Learning with 15 free online courses


This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one -- learning how to get a GPU server online suitable for deep learning -- and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. Prominent review (by Anonymous): "This is really a hidden gem in a field that rapidly growing. Jeremy Howard does an excellent job of both walking through the basics and presenting state of the art results. I was surprised time and again when not only was he presenting material developed within the last year, but even within the week the course was running … You practice on real life data through Kaggle competitions.

Data Science: Master Machine Learning Without Coding [ Udemy 100% Off ]


One of the maximum not unusual troubles freshmen have when jumping into Machine Learning and Data Science is the steep studying curve, and whilst you add to this the complexity of mastering programming languages like Python or R you could get demotivated and lose interest rapid. In this course you may examine the primary ideas of gadget learning the usage of a visible tool. Where you can just drag drop machine mastering algorithms and all different capability hiding the ugliness of code, making it tons extra simpler to comprehend the essential principles. I will "hand-preserve" you as we construct from scratch 2 one of a kind varieties of supervised gadget learning algorithms used inside the real global, across numerous industries and I will explain wherein and the way they are used. The direction will train you the ones fundamental concepts with the aid of implementing realistic sporting events which might be based totally on live examples.

Cool Projects from Udacity Students – Self-Driving Cars – Medium


I have a pretty awesome backlog of blog posts from Udacity Self-Driving Car students, partly because they're doing awesome things and partly because I fell behind on reviewing them for a bit. Here are five that look pretty neat. This is a great blog post if you're looking to get started with point cloud files. The most popular laptop among Silicon Valley software developers is the Macbook Pro. The current version of the Macbook Pro, however, does not include an NVIDIA GPU, which restricts its ability to use CUDA and cuDNN, NVIDIA's tools for accelerating deep learning.

Data Science: Learn Machine Learning Without Coding


One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. The course will teach you those fundamental concepts by implementing practical exercises which are based on live examples.

Modern Artificial Intelligence Infographic - e-Learning Infographics


The history of Artificial Intelligence isn't a long one, around 60-70 years, but the advances in recent years has been huge. The Modern Artificial Intelligence Infographic shows how technology coupled with studies of the human brain have aided in making AI a reality, and a reality we can use everyday. Machines are already intelligent, but we fail to recognise it. When a machine demonstrates intelligence we counter it by saying'it's not real intelligence'. Therefore Al becomes whatever has not been accomplished so far by a machine.