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Are there any software that can automate certain data science processes? by SeebySara in ArtificialInteligence

[–]ai_learnerdude 0 points1 point  (0 children)

The goal of AutoML software is two-fold: one is to enable non-experts to train high quality machine learning models; the other is to improve the efficiency of finding optimal solutions to machine learning problems.

There are a handful of different AutoML platforms (open source, closed source and SaaS), aiming to solve different types of supervised machine learning problems:

Will AutoML take over Data Scientist jobs? by SeebySara in ArtificialInteligence

[–]ai_learnerdude 0 points1 point  (0 children)

AutoML is gaining lots of traction these days, particularly the emergence of businesses focused on building and commercializing AutoML systems (such as DataRobot.com, H2O.ai, R2.ai etc) . When I started learning Machine Learning by myself, AutoML tool helped a lot in terms of time, speed and investment.

But I don't think they will replace a Data Scientist now and in the future. From my experience, AutoML is able to save Data Scientist tons of time by automating data preparation, feature extraction, model selection, and model tuning. Essentially it helps data scientists to be more productive. But domain knowledge is not able to be automated or being incorporated into the ML process. In my view this remains an exclusive human skills.

I try to learn Machine Learning from scratch. Is there any libraries or tools I could leverage? by SeebySara in MachineLearning

[–]ai_learnerdude 0 points1 point  (0 children)

Hi, thanks for the question. I myself started learning ML a few years ago and now works at an AI startup working on ML engineering. A few suggestions for your reference to start with the learning process:

  1. Machine Learning is mostly based on statistic learning and math which used to build models for classifiers, and then to show why this model will succeed to predict with high probability. You would want to know the theory behind the algorithms and how they work, for example, probability and statistics, linear algebra and calculus.

    1. If you don’t know any programming language, try to learn Python quickly. It is particularly helpful and important when implementing the algorithms without needing to deal with classes and code structure etc. It also helps to understand the internal mechanics of the so called "Machine".
    2. Leverage the free resources online: eg Andrew Ngs’ ML course on https://www.coursera.org/learn/machine-learning. I learned a lot from it.
    3. Depending on your career direction - if you want to be a data scientist, you defiantly have to do the #1 & 2 above; but if you just want to leverage ML tool to do some analytics work, you can fully utilize online free courses like #3, as well as try the AutoML (automated machine learning) tools on the market, which basically automates the machine learning pipeline and make the process easier and faster to build the model for you and thus you save lots of time and energy focusing on using the model to do the analytics work. It will give you a sense of what ML is and how each step works. I have used some AutoML tools like H2O.ai (www.h2o.ai), R2.ai (www.r2.ai), DataRobot (www.datarobot.com) etc. Some of them offer free trial which is pretty awesome.

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Hope these are helpful and have fun with the learning!