AI News, What requirements should one need to start machine learning?
- On Thursday, October 4, 2018
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What requirements should one need to start machine learning?
Most machine learning ventures have a fundamentally the same as work process: STEP 1.) Fabricate your machine learning fundamentals by studying some material regarding the subject: a.) Andrew Ng’s Machine Learning lectures are a great start: b.) Machine Learning Summer School: c.) A link to the full playlist is here (Lecture Collection |
f.) “The best machine learning introduction I’ve seen so far.” STEP 2.) Take an online course The main thing I advise somebody who needs to get into machine learning is to take Andrew Ng’s online course.
Courses on Machine Learning for Beginners and Advanced STEP 3.) Some Book suggestions My suggested subsequent step is to get a decent ML book (my run down beneath), read the principal introduction sections, and after that bounce to whatever part incorporates an algorithm, you are interested.
You can, in any case, begin with a simple one, for example, L2-regularized Logistic Regression, or k-means, yet you ought to likewise drive yourself to actualize all the more intriguing ones, for example, SVMs.
It condenses a large portion of the rudiments while presenting the scikit-learn library, which can prove to be useful for execution and further examinations: STEP 5.) Play with some enormous datasets that are openly accessible.
https://www.kaggle.com/ “Kaggle is a platform for predictive modelling and analytics competitions on which companies and researchers post their data and statisticians and data miners from all over the world compete to produce the best models.” – Wiki Kaggle exposes you to a wide range of Machine Learning problems, Kaggle competitions “force” you to code and re-code your solution in the most resource efficient manner possible, making tradeoffs between programmer time, CPU time, RAM etc.Each competition has a forum where competitors help each other tackle the problem.
You should start your kaggle with Titanic why because there are plenty of scripts/problems accessible, you will have the capacity to build diverse sort of models which will likewise enable you to understand some of machine learning algorithms.
Next you can take up interesting subject Facebook Recruiting why because given the easiness of the data structure and the extravagance of the content, you can join right tables and make a prescient calculation on this one.
Go to machine learning events where you can realize what people are doing in talks and get hands-on with hackathons, instructional exercises, and workshops like: The Machine Learning Conference O’Reilly’s Strata Conference PyData Crowdflower’s Rich Data conference PS: Want to know in-depth AI and ML latest resources around the web you MUST see this index page here.
The larger part of your chance in machine learning will be spent attempting to make sense of why an algorithm didn’t work out how you expected or why I got the errors that are ordinary.
- On Thursday, February 21, 2019
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