Reddit reviews Introduction to Machine Learning with Python: A Guide for Data Scientists
We found 5 Reddit comments about Introduction to Machine Learning with Python: A Guide for Data Scientists. Here are the top ones, ranked by their Reddit score.
Hey I have no CS degree and started out knowing nothing about ML! I do a mix of data science and data engineering, my internal clients are data scientists (and biologists!), and my boss is a data scientist. I build applications around models, build some models and applications around them, etc.
A few things that have been helpful:
I have a ton of other books (and a lot of deep learning, but most of the frameworks make it easy peasy), but really after doing the Google crash course, the most helpful thing has been building and training models. You'll need to get familiar with tools like Jupyter notebooks, packages like Pandas, frameworks like TensorFlow, scikit-learn, etc. and maybe some data visualization stuff. I like to use AWS SageMaker, since you can spin up whatever resources you need for training and deployment, and has easy access to other AWS resources like S3.
I can suggest having a look at r/DataScience , they seem to be focused on how to become an actual data scientist and get a job with that.
Machine learning is a tool by the way, which you generally learn while doing other things. I'm personally using ML in the framework of my PhD in Physics. I'll most probably be eligible for jobs as data scientists afterwards. I do know a lot of maths, which is useful to understand deep down what is going on.
Of course there are self-taught data scientists and analysts. I know some people started by e.g. reading around on the web (there are a lot of blogs, open source code, ...) and then participating to competitions on Kagle.
I will make some advertisement for MachineLearningMastery.com, because that blog was very helpful when I started. It's a blog that proposes to learn ML in a top-down approach: start by coding and practising, understand later. And also this book, which you might be able to find on the internet. For people more into theory and who want to see the maths behind it, a 800 pages book on deep learning
(At that point I'm just throwing infos and links in case anyone is interested)
Sarah Guido from Bitly and NYC Python is working on exactly this book:
http://www.amazon.com/Introduction-Machine-Learning-Python-Sarah/dp/1449369413
Glad it's useful! I'll copy some of a reply I gave to somebody who PM'd me about advice for a data science career, because it's pertinent to you:
You need to understand where you want to go -- more science-y or more business-y. See, science-y type of analytics are heavy on the stats, applying really advanced methods to glean some counterintiutive and/or non-obvious insight. Business-y type stuff is digging through the data to understand what it's telling you and to build a bit of a story to figure out what the business is doing, and then measuring success after something changes. Both have their value. Essentially: science side tells you about the data, but the business side tells you how to make decisions based on the data. You'll fall somewhere on that spectrum, so just play to your strengths.
Once you've determined this, you need to learn a few things:
As for some resources, here are some courses I think would be good from MIT CourseWare (full disclosure: I haven't sat through these specific courses, but these are the topics that are important):
You may also want to read up on machine learning. I like the O'Reilly book on it, but there are tons of books out there about it now.
Hope that helps!
Thanks for the suggestion. Is below book good for beginning....
https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413#