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Getting started?

 
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Can you talk a bit about what you would consider best practices when it comes to start out with ML? What might be a good project to get your feets wet with? What are dos and don'ts you have learned?
 
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Hi Tim,

Thank you for your question.

I'm biased so I think that the project in my book is a great one for you to start

With that said, I've trained over 2,000 students on ML, so let me share some recurring themes that resonate with IT professionals:

- ML requires traditional IT (compute, storage, networking) but the practice of ML is different from traditional IT
- ML is more like the scientific process than IT, in ML you analyze data, hypothesize about different models for data, and compare different models for your data
- ML sounds like a zoo of different models but in reality you should study supervised learning (>80% of problems out there) for regression and classification. Make sure you understand these problems and whether they apply to what you are trying to achieve before diving deep into the machine learning algorithms. The worse case scenario here is if you become the expert on using random forests models/algorithms but find out that you need to do ML for image processing.

Last but not least, I can tell you from experience that even if you ask a Stanford PhD in ML to work on a dataset that they have never seen they are not guaranteed to achieve the state-of-the-art results on the 1st try. ML is much more iterative and experimental than traditional IT. So don't get discouraged if something doesn't work, ask for help or find a mentor, and try again!

Best,

Carl.
 
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Carl, I like your reference to yak shaving with regard to choosing a serverless platform. I suppose the metaphor also applies to learning about ML itself. I imagine there are numerous other related topics you'd have to study. That could also lead to you winding up in the zoo shaving a yak. To avoid getting overwhelmed by all the different things you have to learn about, what would be included in the base of knowledge that you recommend people cover breadth-wise in order to lay a firm foundation on which they can build up their skills in ML?
 
Carl Osipov
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Hi Junilu,

Thank you for your question.

I just got asked that same question yesterday! I recommend everyone who wants to get into machine learning to refresh on the basics, so start with:

Essence of Linear Algebra https://www.manning.com/livevideo/3blue1brown-essence-of-linear-algebra
Essence of Calculus https://www.manning.com/livevideo/3blue1brown-essence-of-calculus

Next, it is a good idea to get practical experience with using Python and the following libraries:

NumPy
Pandas

Then, you should be ready to pickup Scikit-Learn, XGBoost and do some practical ML projects.

Best,

Carl.
 
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Thanks, Carl. I look forward to reading more of your book. I'm sure I'll have more questions and will post them on the book's forum at Manning.com. Thanks again for hanging out with us and answering questions this week.
 
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