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

We found 43 Reddit comments about Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Here are the top ones, ranked by their Reddit score.

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

u/chuwiki · 33 pointsr/Python

I'd recommend this book. It's really nice for beginners :D

u/RhoTheory · 33 pointsr/MachineLearning

Grad school for machine learning is pretty vague, so here's some general resources I think would be good for an incoming CS grad student or undergraduate CS researcher with a focus on deep learning. In my opinion, the courses you mentioned you've done should be a sufficient foundation to dive into deep learning, but these resources cover some foundational stuff as well.

  • Kaggle is for machine learning in general. It provides datasets and hardware. It has some nice tutorials and you can look at what other people did.
  • Google has an online crash course on Machine Learning.
  • Hands-On Machine Learning with Scikit-learn and Tensorflow is a great book for diving into machine learning with little background. The O'Reilly books tend to be pretty good.
  • MIT Intro to Deep Learning provides a good theoretical basis for deep learning specifically.
  • MIT Intro to AI. This is my favorite online lecture series of all time. It provides a solid foundation in all the common methods for AI, from neural nets to support vector machines and the like.
  • Tensorflow is a common framework for deep learning and provides good tutorials.
  • Scikit-learn is a framework for machine learning in python. It'd be a good idea to familiarize yourself with it and the algorithms it provides. The link is to a bunch of examples.
  • Stanford's deep learning tutorial provides a more mathematical approach to deep learning than the others I've mentioned--which basic vector calc, linear algebra, and stats should be able to handle.
  • 3Blue1Brown is a math youtuber that animates visual intuitions behind many rather high-level concepts. He has a short series on the math of neural networks.
  • If you are going to be dealing with hardware for machine learning at all, this paper is the gold standard for everything you'd need to know. Actually, even if you aren't dealing with the hardware, I'd recommend you look at the seconds on software. It is fairly high level, however, so don't be discouraged if you don't get some of it.
  • Chris Olah's Blog is amazing. His posts vary from explanations of complex topics very intuitively to actual research papers. I recommend "Neural Networks, Manifolds, and Topology".
u/latetodata · 15 pointsr/learnmachinelearning

I personally really benefitted from Jose Portilla's udemy class on python for Data Science: https://www.udemy.com/python-for-data-science-and-machine-learning-bootcamp. It deals with the machine learning algorithms at a pretty basic level but he does a good job overviewing things and this course personally gave me more confidence. He also wrote a helpful overview for how to become a data scientist: https://medium.com/@josemarcialportilla/how-to-become-a-data-scientist-2d829fa33aba

Additionally, I found this podcast episode from Chris Albon helpful: http://partiallyderivative.com/podcast/2017/03/28/learning-machine-learning

Finally, I have just started going through Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems and I love it. It's very easy to read and applicable: https://www.amazon.com/dp/1491962291/_encoding=UTF8?coliid=I1VIM81L3W5JUY&colid=2MMQRCAEOFBAX

Hope this helps.

u/babyfacebrain666 · 12 pointsr/learnpython

On the flip side I kind of envy you for your confidence in the underlying math... that shit is melting my brain currently.

Check out https://automatetheboringstuff.com/ great starter book for basic python programming with more of an emphasis on just making a basic program vs the underlying data structures or algorithms. Anyone who says they don't still use these programs or an improved version of one is lying lol


For Machine Learning stuffs: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291 the current cause of my brain melting.

If you don't like the idea of a textbook:
http://interactivepython.org/runestone/static/pythonds/index.html

http://www.fast.ai/ (this is EXTENSIVE I've been working on it on-off for like a year)

u/finitedimensions · 11 pointsr/datascience

I glanced at "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurelien Geron and thought it is quite good. But I have not had a chance to read it deeply yet.

​

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291

u/pm_me-your_tits-plz · 9 pointsr/learnpython

I haven't read it myself, but it has been recommended to me. https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291

Edit: PM me if you want a free copy(have it in epub, mobi and pdf)
EDIT: I stand corrected, I was thinking of another book that was azw3 format.

u/fisat · 8 pointsr/MachineLearning

Read Hands on Machine Learning with Scikit-learn and Tensorflow. This book is awesome.

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291

u/Robin_Banx · 7 pointsr/datascience

Almost the exact same trajectory as you - graduated with a psych degree, learned a lot of stats and experiment design, then did the Coursera ML course.

Reading this book is probably the biggest thing that took me from knowing there to doing well in interviews (before that it was just scattered projects): https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291 A second edition is coming out pretty soon, so watch out for that.

If I were doing it today, this is probably the best material out there: https://www.dunderdata.com/ It starts from scratch and gives you an amazing tour of Pandas. Author's also working on a practical Machine Learning book.

u/equinox932 · 5 pointsr/Romania

Vezi si fast.ai, au 4 cursuri foarte bune. Apoi si asta e bun. Hugo Larochelle avea un curs de retele neuronale, un pic mai vechi.

La carti as adauga si The Hundred Page Machine Learning Book si asta , probabil cea mai buna carte practica, da asteapta editia a 2a, cu tensorflow 2.0, are tf.keras.layers, sequential model, practic tf 2 include keras si scapi de kkturile alea de sessions. Asa, si ar mai fi si asta, asta si asta. Nu pierde timp cu cartea lui Bengio de deep learning, e o mizerie superficiala. Spor la invatat si sa vedem cat mai multi romani cu articole pe ML si DL!

u/NaturalDisplay · 5 pointsr/algotrading

Another great book for me was Hands-on Machine Learning with scikit learn and TensorFlow. This one I think is where I started to get some intuition on what some of the ML algo's were actually doing, and he provides lots of material on algorithm tuning.

u/WeoDude · 4 pointsr/datascience

I don't have a tutorial for TensorFlow, but Hands on Machine Learning with Scikit-Learn and TensorFlow (https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_1?ie=UTF8&qid=1500494347&sr=8-1&keywords=hands+on+machine+learning) should basically be the bible of machine learning implementation.

XGboost, the best way I learned it, Is through looking at Kaggles.

u/PostmodernistWoof · 3 pointsr/MachineLearning

+1 for top-down learning approaches. There's so much work going on to democratize use of ML techniques in general software development, that, depending on where you want to go, there's little need to start with the classic theory.

IMHO, the classic ML literature suffers a bit from decades of theorists who never had the computing resources (or the data) to make big practical advances, and it tends to be overly dense and mathematical because that's what they spent their time on.

But really it depends on your goals. Which category do you fall into?

  1. Get a PhD in math, study computer science, get a job as a data scientist at Google (or equivalent) and spend your days reading papers and doing cutting edge Research in the field.

  2. Learn classic and modern ML techniques to apply in your day to day software development work where you have a job title other than "data scientist".

  3. You've heard about Deep Learning and AlphaGo etc. and want to play around with these things and learn more about them without necessarily having a professional goal in mind.

    For #1 the Super Harsh Guide is, well, super harsh, but has good links to the bottom up mathematical approach to the whole thing.

    For #2 you should probably start looking at the classic ML techniques as well as the trendy Deep Learning stuff. You might enjoy:

    https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291

    as a place to start and immediately start playing around with stuff.

    For #3 any of the TensorFlow getting started tutorials are good, along with all of Martin Görner's machine learning/deep learning/TensorFlow "without a PhD" videos on YouTube. Here's one of the more recent ones:

    https://www.youtube.com/watch?v=vaL1I2BD_xY
u/DecisionTreeBeard · 3 pointsr/datascience
u/Jaydii- · 3 pointsr/learnmachinelearning

Hi,

Not trying to sell anything here, but I've been reading this book : https://www.amazon.ca/Aur%C3%A9lien-G%C3%A9ron/dp/1491962291/ref=mp_s_a_1_1?keywords=machine+learning&qid=1568912246&sprefix=machibe+l&sr=8-1

I think it is a pretty complete book that covers a lot and a good first step into ML.

Plus there are plenty of examples using Python.

GL

u/brandonhotdog · 3 pointsr/videos

My previous video on AI did go into a lot more depth on neural networks but still wasn't enough to build your own. (There's only 2 vids on my channel so far so it's just the other one). I will defiantly consider making a video for my fellow devs on AI but for now you should check out https://www.youtube.com/watch?v=32wtJZ3yRfw&list=PLX2vGYjWbI0R08eWQkO7nQkGiicHAX7IX if you want to learn how to implent AI into a Unity3D project. If Unity3D isn't for you then you can read:

https://www.amazon.co.uk/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_3?keywords=machine+learning+hands+on&qid=1563498470&s=gateway&sr=8-3

Also thanks for the support!

u/raijenki · 3 pointsr/brasil

Olha, não posso falar muito em termos de qualidade dos cursos. O grande lance é o nome que você vai botar no currículo: Estácio é praticamente sinônimo de massificação do ensino superior, o que não gera um destaque para quem te avalia posteriormente. Em compensação, eu não conheço a PUC-MG, mas já ouvi falar na PUC-SP, PUC-RJ e na PUC-RS, que em geral são boas escolas - e provavelmente pensaria o mesmo da PUC-MG. Isso agregaria mais valor para ti.

Sobre o currículo em si:

  • Estácio: O curso é mais forcado em negócios (o que o pessoal chama de "Analytics") do que em Ciência de Dados em si. Disciplinas de "Orientação de Carreira", "Governança Corporativa", "Consultoria", "Desenvolvimento Sustentável", "Finanças Empresarias" dentre outras compõem boa parte do curso e, para mim, são perda de tempo - se quer estudar negócios, vá para uma escola de negócios em um curso de negócios. Não há menção ao aprendizado de Máquina no curso ("Teorias Analíticas Avançadas"? "Tecnologias Avançadas"? Que porra são essas?).
  • PUC-Minas: Diferentemente do acima, o curso é um misto de engenharia de dados com ciência de dados. Você supostamente aprenderá Hadoop, Spark, RDBMS, Python, R, e os algoritmos mais tradicionais. É coisa demais para pouco tempo

    Se eu tivesse que escolher, iria de PUC. Mas quer aprender de verdade? Faça o curso de Machine Learning do Andrew Ng no Coursera e pegue o certificado, vale muito mais do que isso e tem uma hands-on approach. O Andrew é professor de Stanford e tem uma puta didática.

    E se quiser aprender por aprender, compre esse livro: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_1?crid=35OQXMZA5U4H6 Nova edição sai dia 5 desse mês.
u/Denis_Vo · 3 pointsr/algotrading

I would highly recommend to read the following book

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=mp_s_a_1_2?keywords=machine+learning&qid=1566810016&s=gateway&sprefix=machi&sr=8-2

I think it is the best one about ml/dl. Not sure that they already updated the tensorflow examples to tf 2.0 and keras.

And as tensorflow includes keras now, and has perfect pipeline for deploying your model, i think it is the perfect choice. :)

u/bagoffractals · 2 pointsr/serbia

Knjiga 1

Ova meni deluje dobro za pocetak, ima jos jedna gde pravis sve ove algoritme od nule ali sam zaboravio naziv, dopunicu posle post. Elem imas gomilu predavanja sa faksa po netu pa mozes da bacis pogled i na to.

u/Gimagon · 2 pointsr/neuralnetworks

I would highly recommend Aurélien Géron's book. The first half is an introduction to standard machine learning techniques, which I would recommend reading through if you have little familiarity. The second half is dedicated to neural networks and takes you from the basics up to very results from very recent (2017) literature. It has examples building networks both from scratch and with TensorFlow.

If you want to dive deeper, the book Deep Learning is a little more theoretical, but lacks a lot of low level detail.

Joel Grus's "Data Science From Scratch" is another good reference.

u/lbiewald · 2 pointsr/learnmachinelearning

I agree this is a missing area. I've been working on some materials like recent videos on Transfer Learning https://studio.youtube.com/video/vbhEnEbj3JM/edit and One Shot learning https://www.youtube.com/watch?v=H4MPIWX6ftE which might be interesting to you. I'd be interested in your feedback. I also think books like https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=pd_lpo_sbs_14_t_1?_encoding=UTF8&psc=1&refRID=3829RHN356ZXBEBP0KF3 do a good job of bridging some of this gap. Reading conference papers is a skill that takes practice and a strong math background.

u/Dansio · 2 pointsr/learnprogramming

Then learning Python would be very useful for you. I have used the book called Automate the Boring stuff (Free).

For data science and machine learning I use: Data Science from Scratch and Hands on Machine Learning with Scikit-learn and Tensorflow.

For AI I have used Artificial Intelligence: A Modern Approach (3rd ed.).

u/Dracontis · 2 pointsr/datascience

I'm a beginner too, so I can't give you end-to-end solution. I'll try to describe my path.

  1. You'll definetly need some statistics background. I've taken free Inferential and Descriptive Statistics courses from Udacity.
  2. I've decided to go further in Machine Learning. There I've got two choices Machine Learning A-Z™: Hands-On Python & R In Data Science and Machine Learning from Andrew Ng. I've decided to take second one and I'm on the fifth week now. It's really good for ML basics and theory, but programming assignments is horrible. So I think I'll have basic understanding of what's going on, but I will have near to no practical skills. That's why I asked question here about scientific advisory here.
  3. After I finish course, I plan to read Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems to boost knowledge of algorithms on the python.

    I have no idea what I'll do next. Maybe, I'll took several courses and nanodegrees on Coursera. Maybe I'll find guidance and start getting hands on experience on a real project. It's not so hard to start learning - it's hard to find purpose and application of your knowledge.
u/officialgel · 2 pointsr/learnpython

There is a good book, but not easy to ingest if you don't know your way around python much:

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291

There are also many many walkthroughs on specific things like image recognition. Just search for them.

Even if these things aren't what you want to do - It's good to do them for the experience and knowledge while working up to the project you have in mind. They all have elements which you would need to understand for your own project.

It's all an ongoing thing. Nothing is a waste of time if you're learning and there is no rush right?

u/Bayes_the_Lord · 2 pointsr/datascience

Hands-On Machine Learning

There's a new edition coming out in August though.

u/davincismuse · 2 pointsr/learnmachinelearning

If you already know Python and are familiar with the numpy, pandas, matplotlib and jupyter notebooks, then this book does a great job of teaching basic Machine Learning and more advanced Deep Learning concepts.

Hands on Machine Learning with Scikit learn and Tensorflow by Aurelion Geron.

https://www.amazon.in/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291

One caveat though - Tensorflow has undergone a lot of changes since this book came out, so you might have to tweak the code a bit.

Github Repo for the code - https://github.com/ageron/handson-ml

u/ActuarialAnalyst · 2 pointsr/actuary

Yeah. If you want to be good at like programming-programming I would read this book and do all of the projects: https://runestone.academy/runestone/books/published/fopp/index.html If you take like algorithms class you will probably get to use python.

If you want to be good at data analytics I would read "R for data science" if you want to use R. If you learn python people like this book for data science learning https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=pd_sbs_14_2/145-5658251-1609721?_encoding=UTF8&pd_rd_i=1491962291&pd_rd_r=4e33435c-cc98-4256-9c50-6050e79b7803&pd_rd_w=ejSx8&pd_rd_wg=Ter1m&pf_rd_p=d66372fe-68a6-48a3-90ec-41d7f64212be&pf_rd_r=3X23DYAJ2ZMCKP9AA1Z4&psc=1&refRID=3X23DYAJ2ZMCKP9AA1Z4 .

These books are kind of different though. The python book is much more focused on theory and will help you less in the workplace if you aren't actually building predictive models (at least I think based on table of contents).

u/ryanbuck_ · 2 pointsr/learnmachinelearning

It sounds like you have identified your weakness. Presently, that is programming in python, and using the sklearn library.

I would recommend taking a MOOC on python first. Lynda.com has a free trial and python videos. datacamp is another good start. It has a free trial and mayybe some python basics, but definately something on sklearn. and you can get some pandas training or R training there. (the kaggle libraries, most likely).

At that point, if you are going the tensorflow route, Aurelion has a great hands-on book called Learning Tensorflow with sci-kit learn

If you’re going with pyTorch I dunno.

Your mileage is going to vary, you could always use a book to learn python, or whatever.

Just make sure you learn to program first, you’d be surprised how much 2 weeks of very hard work will earn you. Don’t expect it to be ‘easy’ ever tho.

Also, if you’re not formally educated in statisics, keep an eye out for statistics advice until you have the time to work on it. (like in a MOOC, course, or blog). Learning some real analysis will make understanding the papers a real possibility (once again it will probably never be easy)

It is truly stunning how many years of preparation it takes to become competent in this. It’s a lovely science, but the competent ones have generally been on a mathematical/science track since 5th grade. Doesn’t mean we can’t become competent but it takes time. Imagine the equivalent of an undergraduate degree just devoted to ML and you’re about there.

u/puddlypanda12321 · 2 pointsr/learnprogramming

I really enjoyed ‘Hands-On Machine Learning with Scikit-Learn and Tensorflow.' It has a great balance between theory and application of common machine learning techniques as well as best practices when dealing with data (for example how to avoid overfitting).

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291

u/bdubbs09 · 2 pointsr/learnmachinelearning

Hands-On Machine Learning is pretty much a staple to start out with.

u/doddyk96 · 1 pointr/datascience

Thank you so much for your reply. I actually do plan on taking Andrew Ng's course just cause the book I am talking about is very limited to Python but I've heard great things about it. However, the Stanford course I was referring to was the Statistical Learning course based on the ISL book.

Yes I plan on doing some kaggle challenges once I feel comfortable with my skills to build up my portfolio or see if I can find some other novel projects to work on.


Ideally I'd like to be in a data science consultancy type role where I get to work on different kinds of projects and don't necessarily need very specialized domain knowledge. But at this point I think more direction as to what kind of roles exits would also be helpful. I just don't know what the field is actually like and I've never really met anyone doing data science for a living.

Thank you again for your reply. It was very helpful.

u/[deleted] · 1 pointr/statistics
  1. Data Analyst may or may not use sophisticated statistical methods. If they do a lot they are probably underemployed. Most Data Analysts use SQL, Python or R, and various business intelligence software (Tableau) to do data manipulation and summarization. Statisticians are typically designing studies, analyzing the data, summarizing and presenting results. They will have a much deeper understanding of theory.

  2. SQL is important but R or Python is much more. Your data may be in files, relational databases, or even NoSQL databases so being able to pick those things up as needed is more important than one specific language.

  3. Starting out in a business intelligence position or a data analyst position will be easiest and would give you good experience but could get boring quickly. With a masters you would technically qualify for a statistician or even data scientist position which is what you’re going to eventually want. As long as you feel confident about the job qualifications go for those instead.

    4 and 5. This really depends on the industry or domain, but good free books are: ISLR , Forecasting: Principles and Practice . The first is more general. I would focus there first. An inexpensive excellent python book: Hands on machine learning with sci-kit learn and tensorflow

  4. Depends on job, but I would say stats is more important as long as you can pick up programming as needed. I was given some very good advice that knowing how to analyze the data is more important than what technology you know.

  5. Classification or Regression. Kaggle has lots of competitions and data.

  6. There are jobs but lots of unqualified applicants due to hype. I would say there is lots of opportunities for qualified individuals.
u/linked0 · 1 pointr/Python

A really nice and useful site!
And I think you should add Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems for Machine Learning as soon as possible.

u/alzho12 · 1 pointr/datascience

Step 1: Harvard CS50 and CS109

Step 2: Hand's on ML

Step 3: Build a Portfolio


Figure the rest out as it comes up

u/njack42 · 1 pointr/OMSCS
u/naresha5 · 1 pointr/learnmachinelearning

I didn't give this a try. If you are looking for practical applications, you can check "Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems". I heard good reviews about this book
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_1?ie=UTF8&qid=1509062586&sr=8-1&keywords=hands-on+machine+learning+with+scikit-learn+and+tensorflow

It is there in safaribooks. You can use the free trial to see the contents (https://www.safaribooksonline.com/library/view/hands-on-machine-learning/9781491962282/)

u/Mabb_reddit · 1 pointr/artificial
u/ttelbarto · 1 pointr/datascience

Hi, There are so many resources out there I don't know where to start! I would work through some kind of beginner python book (recommendation below). Then maybe try Andrew Ng's Machine Learning Coursera course to get a taste of Machine Learning. Once you have completed both of those I would reassess what you would like to focus on. I will include some other books I would recommend below.

Beginner Python - https://www.amazon.co.uk/Python-Crash-Course-Hands-Project-Based/dp/1593276036/ref=sr_1_3?keywords=python+books&qid=1565035502&s=books&sr=1-3

Machine Learning Coursera - https://www.coursera.org/learn/machine-learning

Python Machine Learning - https://www.amazon.co.uk/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_7?crid=2QF98N9Q9GCJ9&keywords=hands+on+data+science&qid=1565035593&s=books&sprefix=hands+on+data+sc%2Cstripbooks%2C183&sr=1-7

https://www.amazon.co.uk/Data-Science-Scratch-Joel-Grus/dp/1492041130/ref=sr_1_1?crid=PJEJNNUBNQ8N&keywords=data+science+from+scratch&qid=1565035617&s=books&sprefix=data+science+from+s%2Cstripbooks%2C140&sr=1-1

Statistics (intro) - https://www.amazon.co.uk/Naked-Statistics-Stripping-Dread-Data/dp/039334777X/ref=sr_1_1?keywords=naked+statistics&qid=1565035650&s=books&sr=1-1

More stats (I haven't read this but gets recommended) - https://www.amazon.co.uk/Think-Stats-Allen-B-Downey/dp/1491907339/ref=sr_1_1?keywords=think+stats&qid=1565035674&s=books&sr=1-1

u/DaveVoyles · 0 pointsr/cscareerquestions