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

@machinelearnbot

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.


[N] Andrew Ng announces new Deep Learning specialization on Coursera • r/MachineLearning

@machinelearnbot

Even though I did not follow his older courses, they seem really appreciated, at least on this subreddit. I hope these new ones will set an even higher standard. That way, newcomers may share an identical set of notations, principles and methodologies so we can all focus on other tasks, such as visualization. You will practice all these ideas in Python and in TensorFlow. What do you guys think of this choice?


Digitalizing business: The difference two letters can make - TotalCIO

#artificialintelligence

Thanks! We'll email you when relevant content is added and updated. We'll email you when relevant content is added and updated. We'll email you when relevant content is added and updated. We'll email you when relevant content is added and updated. If you answer the question with another -- Does it matter?


Machine Learning Is Everywhere: Netflix, Personalized Medicine, and Fraud Prevention Udacity

#artificialintelligence

The overall goal is to target treatment specifically to each individual so that clinical outcomes for that individual are optimized. One direction of attack is to use patient data to discover decision rules which specify the treatment to use as a function of a vector of features from the patient. Regression and classification are important statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning can help with this because of its ability to artfully handle high dimensional feature spaces with potentially complex interactions.


[Discussion] I am following Andrew Ng's Coursera course. Is there an entry course to better follow it? • /r/MachineLearning

@machinelearnbot

I can't offer much in terms of other entry level recommendations, but I can recommend you learn to utilize the resource pages on the coursera course. The way the andrew NG course is set up is that you more or less try to have an idea of how these algorithms work at a conceptual level through the videos, then when you go to programming assignments, you can skip a lot of the prep work and focus on implementing the machine learning algorithms. Now those algorithms might be a little hard to follow at first, which is okay and expected, and that's where the lecture notes and/or wiki come in. From the wiki you can more or less translate the math formulas into code syntax and the assignments are more or less complete. The weeks build off each other so as you learn how to do one part, they do a little less prep work for you so you have to learn how to do another part, and so forth.


This Week in Machine Learning, 17 June 2016 -- Udacity Inc

#artificialintelligence

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.


This Week in Machine Learning, 3 June 2016 -- Udacity Inc

#artificialintelligence

This week's top Machine Learning stories, including AI agents that compose music, watch movies, surf Facebook, and more! Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning!


Machine Learning Is Everywhere: Netflix, Personalized Medicine, and Fraud Prevention Udacity

#artificialintelligence

The overall goal is to target treatment specifically to each individual so that clinical outcomes for that individual are optimized. One direction of attack is to use patient data to discover decision rules which specify the treatment to use as a function of a vector of features from the patient. Regression and classification are important statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning can help with this because of its ability to artfully handle high dimensional feature spaces with potentially complex interactions.


Udemy – Face Detection -Master Open CV with Digital Image Processing [50% off]

#artificialintelligence

First of all let me tell you what is Open CV and what are the things that we can do using OpenCV. OpenCV is a open source C library for digital image processing and computer vision, which can be used to create real time face recognisation and using it with embedded robotics and micro controllers for purpose like differentiating a specific color from an image having various colors. Solution to all this we will cover in this course. "Few years back, I started learning programming and spent couple of months just to learn the basics. Then, for again a couple of months I spent my time learning advance of Open CV.


Online Submodular Set Cover, Ranking, and Repeated Active Learning

Neural Information Processing Systems

We propose an online prediction version of submodular set cover with connections to ranking and repeated active learning. In each round, the learning algorithm chooses a sequence of items. The algorithm then receives a monotone submodular function and suffers loss equal to the cover time of the function: the number of items needed, when items are selected in order of the chosen sequence, to achieve a coverage constraint. We develop an online learning algorithm whose loss converges to approximately that of the best sequence in hindsight. Our proposed algorithm is readily extended to a setting where multiple functions are revealed at each round and to bandit and contextual bandit settings.