Reddit reviews Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Hands-On Machine Learning . The 2019 version. It’s one of the best ML books I have come across.

>Há espaço pra gente sem formação?



sim

contanto q saiba programar, um pouco de estatistica, ETL e o mais importante: saber se comunicar e resolver problemas

recomendo fortemente o dataquest, atualmente são os melhores 1k reais que alguém pode investir em educação atualmente independente da área

se preferir estudar por livro https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646 + algum curso de programação basica à sua escolha

>Dão prioridade para Estatísticos de formação

hmm, depende da empresa, essa ideia que ciencia de dados é complexo e precisa de phd já está acabando (exceto se for engenheiro de machine learning, que são poucos no brasil)

Thanks and glad that you found it useful, I will try to create a concise learning path for you.

(1)Start from learning the python syntax . Automate the Boring Stuff with Python is freely available on web and one the most interesting ways to learn python. First 14 chapters are more than enough and you can skip the rest. This is bare minimum you should know and then you can follow the topics below in any sequence.

(2)The book Think Stats: Exploratory Data Analysis in Python is good way to learn statistics and data analysis using python. This is also freely available in HTML version.

(3) For core Machine learning concepts you can solely rely on Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow which in my opinion is the best book on subject. This is second version of the book with updated content and the first version had overwhelmingly positive reviews. It explain the math, theory and implementation of common ML algos in python. The book is divided into two parts Traditional ML models and Deep Learning, You can concentrate on first part and leave deep learning for later depending on your appetite and use case. Many ML problems can be solved without deep learning.

(4)You can supplement the book with coursera machine learning course taught by Andrew Ng who is one of the best teachers on this subject and he has ability to make complex mathematical concepts sound simple. The course is available freely, though the exercises are meant to be done using Octave(Matlab like ). But you don't need to learn octave, there are github repositories for solution using python in case you want to attempt those.

I have no Idea of bootcamp related stuff but the content I mentioned should be more than enough to get you started with Data Science journey.

Udacity has data science nanodegree programs and content wise it looks good, but I have no experience of the quality of the program,

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow-dp-1492032646/dp/1492032646/ref=mt_paperback?_encoding=UTF8&me=&qid=1568461722

The kindle edition and ebooks are out but it looks like the paperback isn't out quite yet.

If you can wait for the release of this book:
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/

First edition is amazing.

I believe so.** As u/LanXlot said, Google Colaboratory is free to use for research and learning. Also, you can sign up to use better machines with Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform. All three cloud services allow you to access a virtual machine with all the processing power you'd ever want for a small fee. While taking an NLP class I used AWS to run huge programs for less than $5/hour. I would write most of my program locally with a commented section to enable a GPU when I was ready to run it on the virtual machine.

I can also tell you Amazon has a free tier that was better than my computer for most projects when I started the course and I used it as often as I needed to as well. There was about a 10 hour learning curve to get everything running easily, but overall it was a fun experience.

Best of luck!

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**EDIT: I believe it is worth experimenting with deep learning regardless of what computing ability you have at home.

It may be worth your time to purchase Deep Learning with Python if you want to learn the basic concepts of deep learning from a programmatic, practical perspective. Another good book to start with may be Hands-on Machine Learning with Scikit Learn, Tensorflow, an Keras. There is more to AI and machine learning than just deep learning, and basic machine learning techniques may be useful and fun for you.

Do you mean this one? The new content is Keras, not PyTorch.

https://automatetheboringstuff.com

http://en.wikibooks.org/wiki/Non-Programmer%27s_Tutorial_for_Python_3

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646

Both are overpriced unless someone else is paying for it.

I have some recommendations on books to get up to speed.

Read this book:

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

This author does a really good job going through a lot of different algorithms. If you can wait, then go with this book instead - which is by the same author but for TensorFlow 2.0, which is pretty recent and also integrated Keras. It's coming out in October.

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

You can get good datasets on Kaggle. If you want to get an actual good foundation on machine learning then this book is often recommended:

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

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As for staying up to date, it's hard to say because "machine learning" doesn't refer to a single thing, there are a lot of different types of machine learning and each one is developing fast. For example, I used to be pretty into recurrent neural networks for sequence data. I haven't kept up with it lately but I remember about two years ago the hotness was all about LSTM neural networks, but then a simplified gate pattern was shown to be just as good with less training and that became big (name is escaping me right now...). Then the last time I took a look, it looked like people were starting to use convolutional neural networks for sequence data and getting great results on par or better than recurrent neural networks.

The ecosystem is changing fast too. Tensorflow uses (used?) static graph generation, meaning you define the network before you train it and you can't really change it. But recently there was more development on dynamic neural networks, where the network can grow and be pruned during training - and people were saying this is a reason to go with PyTorch instead of Tensorflow. I haven't kept up, but I heard from a friend that things are changing even more - there is this new format called ONNX that aims to standardize information about neural networks; and as I've mentioned earlier in this post, TensorFlow 2.0 is coming out (or out already?).

I'm not doing too much machine learning at the moment, but the way I tried to get new information was periodically looking for articles in the problem type I was trying to solve - which at the time was predicting sequences based on sparse multidimensional sequence data with non-matching step intervals.

If you read the TensorFlow book I linked above, you'll get a great overview and feel for what types of problems are out there and what sort of ML solutions exist now. You'll think of a problem you want to solve and then it's off to the search engines to see what ideas exist now.