What is Reddit's opinion of Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems?

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I agree, but I will add a crucial part: learn python to do specific things. Don’t learn the language for the sake of knowing python. Learn it in an applied way, which means doing end-to-end projects.

OP, you’re in a perfect position to be able to level up quickly because you have data to work with: the data at your job.

I recommend using python to do stuff you mentioned already doing: pull data, clean it up, make some visualizations, build models using scikit-learn/statsmodels, report model comparisons in a visual way.

Hand-in-hand with all this, I would get one of the many “machine learning with python” books and work through it using the data from your company. Not only will you learn the material faster because you are contextualizing the new concepts with data you understand, you’ll be able to impact your company with the assets you create as you learn. I found this book to be particularly nice, though you have many options.

Hope this helps!

I have recommend this book to all my students: https://www.amazon.com/dp/1492032646?ref=ppx_pop_mob_ap_share

It covers high level usage of machine learning libraries and a wide variety of concepts. It also has a github which hosts all of the example code in Jupiter notebooks which is neat. From there you can drill down on any concepts that interest you.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems https://www.amazon.com/dp/1492032646/

Hands down the best book in the field. Talks about both Keras and raw TensorFlow.

How is your math? If you're comfortable with multivariable calc, linear algebra, and some statistics (mostly the basics) then you should be ok. If not, it is helpful (but not totally necessary) to refresh.

This book that helped me out a ton when I was first learning TF, it has hands-on projects which cover SVMs, neural nets, computer vision, NLP, and RL. It doesn't shy away from the mathematical rigor, so you actually come away with a theoretical understanding of the algorithms, which makes a lot seem less like a black box, and because the projects are hands-on you actually know how to apply the theory into actual code.

I recommend this book in machine learning. It was what got me started :)

> rather than as a user of well known and used networks and techniques.

I see what you're getting at. Unfortunately ML is very heavy on the theory aspect. It's a bit more applied math than just comp sci, so knowing what's going on under the hood is very important, even in a high level overview kind of setting.

I read the first edition of this book, Hands-on Machine Learning and I found it to be incredibly helpful in easing me into both the theory and practice of ML from basic concepts. And just well written enough to be a pretty enjoyable read

Indeed, you can preorder it from Amazon here

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I've currently reading it, if you have subscriptions to safaribooksonline the 2nd version is already available in draft mode, and find that it contains just enough theory + hands on to keep things interesting.

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The second chapter dive straight into an End to end so that you get the overview picture right away instead of spending too much time on any particular area.

I would actually highly recommend that book you linked by Jake Vanderplas. I have it in paper back, read it a couple years ago, and still reference it from time-to-time.

IIRC it doesn’t get in to tensorflow and neural nets and all that stuff through. I think for that you might want to check out this book (haven’t read it entirely but I see it recommended a ton).

I would highly recommend Coursera.

Look up Machine Learning Specialization by Andrew Ng. I've just finished it and it's perfect for beginners (me).

It's not math heavy at all really, but I have been studying Calculus, linear algebra and applied mathematics at uni for the past 3 years and it certainly helped. I know that probabilities and statistics is also helpful.

It does require a subscription though which costs me £40 per month, but they are high quality courses taught by experts, so I would say the value for money is worth it.

It recommends 10hrs/week to finish the full specialization in 3 months, but it can be done faster if more time is committed as they are self paced.

I would say though that 10hrs/week is optimistic if you like to take lots of notes and don't like to move on until each concept is fully understood.

There are quizzes, lab tasks, and lab assignments in which there are exercise where where you must complete certain parts of functions, such as a cost function or gradient descent algorithm.

There is also a helpful forum to ask as many questions as you like which I used on a few occasions.

Also away to get this book as it's supposed to be good.

https://www.amazon.co.uk/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=asc_df_1492032646/?tag=googshopuk-21&linkCode=df0&hvadid=375498709181&hvpos=&hvnetw=g&hvrand=13855415143965638771&hvpone=&hvptwo=...

Hope some of that was helpful, all the best on your ML journey.

Ya. I found this book to be very helpful when I first started. dataset preprocessing is actually a very in depth field with many different algorithms and processes to prep your data. I am surprised your search turned up small results. I also have an PDF copy of another book that I might be able to send if you want. It's much more in depth and covers data preprocessing and topics like that.

For learning python basic data amp and code academy are fine. For ai you need to take Andrew ng's machine learning and AI courses on Coursera. They are all free and his class shows the code and math behind various algorithms.

I also would suggest signing up for Udacity to take some free classes if you'd like a higher level explanation, they are also free. Udacity also offers paid subscriptions, and in the past I've created some accounts to do the week or month trial to sign up for a program and spend every night watching the lectures and do the program projects.

While learning about ML a good book is from orielly Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems https://www.amazon.com/dp/1492032646/ref=cm_sw_r_apan_glt_i_MX60RAA716SQZPE90QZJ

No worries! I would suggest getting this book or similar and working through it. Doing your own project first is fun, but most likely there are a dozen “gotchas” that you are going to miss

I hear you about not wanting to do a whole course, but it'll help to know Python before you get into the ML since pretty much every Python ML book starts off assuming you understand the syntax and structure of the language before you dive into the code. Since you already know a few languages, you could probably pick that up in a weekend.

That Intro to ML book is a good start. Once you finish that, I'd check out this one, Hands-on ML. This is book is fantastic but it's going to assume you know Python.

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 1st Edition was my first book. You might want to look at the most recent version.

ML is a subset of AI.

If you have some knowledge of statistics it should take about a week to learn the basics of ML. Start learning how linear regression works and then go from there.

I’m using the book “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” and so far it has been pretty helpful.

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_1?dchild=1&keywords=Hands-On+Machine+Learning+with+Scikit-Learn%2C+Keras%2C+and+TensorFlow%2C+2nd+Edition&qid=1602413998&sr=8-1

I‘ll send you a standardised path to become employable/useful via PM, but essentially: - A Python corse to learn the basics (codecademy from memory) - A general data science course on Udemy - Some Machine Learning courses and a textbook, though I’d recommend Aurelian Gueron’s book - Some deep learning specific resources (I’d recommend fast.ai’s ML and DL courses) - An AWS solution architect associate certification to get a taste of the cloud - Projects. I built a data imputation library, got involved with a couple research papers after seeing someone speak at a meet up, and a few Kaggle projects.

Once I landed the role, just kept learning, doing more courses, reading more papers. Python/software engineering has been the thing that has taken the most time overall. If anyone else wants the resources, just PM me.

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

This is the best hands on book and will help you in understanding Machine Learning as well as Deep Learning. With this I would suggest the Coursera - TensorFlow in Practice Specialization and Introduction to Deep Learning by MIT.

Also, the documentation! That's a must.

A PhD in physics will be a great education credential. If you want to go data science, brush up on your stats and ml knowledge for interviews. Books like An Introduction to Statistical Learning and Hands-on ML (part 1) are great resources for this. Make sure you have some coding knowledge in R or python and SQL. For data science emphasize stats and ml knowledge over coding. For data engineer coding and tech skills matter most. There is huge opportunity in data engineer and it pays well so don’t look past it. Lots of competition for data science jobs right now.

For data science:

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

For data engineer:

https://www.amazon.com/dp/B06XPJML5D/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1

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

I was a SWE for 3 years and then transitioned into a MLE role. Been in the ML space for 2 years now and I’m loving it. Your first point is the most important one. Running ML in production is like 95% SWE. There is a significant shortage of SWE skills in the ML space. Having a masters will definitely help get you through the screening process for most companies. As for what you need to know, this varies significantly from position to position. Personally, I would recommend reading 2 books. Hands-On Machine Learning and Deep Learning for Coders with fastai and PyTorch. In my opinion, if you understand the material in these books very well, you will be well suited for most MLE positions.

Well, I have read the book below and a few other resources.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_6?crid=1CIL9ZC0E0J3G&dchild=1&keywords=introduction+machine+learning&qid=1622981241&sprefix=introduction+machine+%2Caps%2C199&sr=8-6

The issue is that Prado has a very mathematical approach, and unless you have developed the intuition going through simpler examples, it will not make much intuitive sense. For instance, in section 5.4, he is applying the backshift operator to a matrix of features and then proceeds to relate that to the binomial expansion. Even for someone familiar with both concepts, it is hard to grasp the intuition behind that. There are several such examples.

These two books are VERY good starting points for Machine Learning: 1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646

1. An Introduction To Statistical Learning (ISLR): http://faculty.marshall.usc.edu/gareth-james/ISL/

If the math isn’t giving you problems then I think the issue is that you just don’t have a good intuition of how the algorithms are supposed to behave. I think your best bet is reading through ISLR for a more in depth understanding of various learning algorithms. Then I think you should be able to implement at the least the basic algorithms (Linear and Logistic Regression) from scratch.

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

la biblia de machine learning, y un poco de contexto

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=pd_lpo_14_t_0/132-8288571-1969948?_encoding=UTF8&pd_rd_i=1492032646&pd_rd_r=4b6fe4e1-2140-46e7-b75e-f27c3b0ee7d7&pd_rd_w=ESZpn&pd_rd_wg=pRBJV&pf_rd_p=a0d6e967-6561-454c-84f8-2ce2c92b79a6&pf_rd_r=0HZX1HXJKFVYWWMX2EEZ&psc=1&refRID=0HZX1HXJKFVYWWMX2EEZ This is the book you want if you know calculus (and python, though I suppose you could learn that through the examples if you have a base in coding) already. This helped me learn machine learning and how to apply it to data science more than my college courses.

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

1,523 ratings
4.8 out of 5

https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413/

300 ratings
4.5 out of 5

• Hands-on Machine Learning with Sci-kit Learn, Keras and TensorFlow

or

• Python Machine Learning

then

• Deep Learning with Python

Sure, but unfortunately they are python focused.

For an introduction into machine learning/data analysis approaches Hands on Machine Learning is great.

Deep reinforcement learning in Action is a great resource for some of the background of reinforcement learning, provides code examples of some of the agents and does a good job of explaining them. It also introduces setting up environments so you can test out the agents and see how they perform.

Also, you cannot recommend books on reinforcement learning without mentioning this book. This will provide you all of the background needed to understand the concepts underlying reinforcement learning. Also, there are github repositories providing the code for each chapter. This is in python but I'm sure Matlab equivalents can also be found.

Lamentablemente en la facu no vi practicamente nada de data science, solo una materia relacionada a redes neuronales. La mayoria lo aprendi haciendo partes de cursos o leyendo articulos, y obviamente practicando mucho.

No soy de aprender desde libros, pero este en algun momento lo lei y esta bastante bueno. Sino este no lo lei pero vi que muchos lo recomiendan para arrancar.

Ova knjiga sadrži odlične osnove, kad savladaš i razumeš većinu stvari, na odličnom si putu: https://www.amazon.ca/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=pd_lpo_1?pd_rd_i=1492032646&psc=1

Takođe, odlična knjiga za manipulaciju podacima u Pythonu, nešto što će ti biti jako bitno: https://www.oreilly.com/library/view/python-for-data/9781449323592/

Ovaj kurs se jedan od najpoznatijih za ulazak u DS: https://www.coursera.org/specializations/machine-learning-introduction#courses

Probaj da se igraš sa datasetovima na Kaggle-u kada stekneš neko znanje.

Verovatno ćeš dobiti ovde još dobrih odgovora, pa ti uzmi izvore koji ti najviše znače. Shvati ovo kao dugačak put, i videćeš da sam DS im mnogo grananja pa nemoj da te obuzme da moraš sve da znaš. Niko ne zna sve. :) Uči jednu po jednu stvar. Ovo je dugačak proces.

Hands On Machine Learning.

It's a book, here you go: https://www.amazon.com.au/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646

Highly recommend it to get started with ML in Python.

Für Data Science würde ich das empfehlen:

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

Grundlagen (kostenlose Bücher) praxisnäher mit Beispielen in R (gibt Python Repos "übersetzt" mal googlen)

etwas mathelastiger

I recommend the book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron. It's in my "must-have" list of ML books. It has exercises and little projects you can follow in every chapter. It's very hands-on and by the end of the book, you'll have projects you can showcase on your resume/github.

I also heard good things about fast.ai from multiple people, but haven't tried it for myself. It's next on my list. Maybe watch the first few lectures and see if you like them.

You can't go wrong with the book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron. It's in my "must-have" list of ML books.

I recommend hands on ML by O'Riley

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

Not a self driving engineer directly, but a deep learning research scientist for a FAANG company. But I have worked with self driving projects on their deep learning models and training strategies.

Most jobs in the field will like to see a masters in something quantitative (CS, physics, math), though for entry level engineer positions, a bachelors is often enough. In terms of what to study, we look for a deep understanding of statistics, probability theory, optimization, and high performance computing. Some recommended books:

Hands-On Machine Learning

Deep Reinforcement Learning Hands-On

Deep Learning

The Elements of Statistical Learning

Machine Learning: A Probabilistic Perspective

Pattern Recognition and Machine Learning

Not a self driving engineer directly, but a deep learning research scientist for a FAANG company. But I do help to advice self driving companies on how to design their models and training strategies.

Most jobs in the field will like to see a masters in something quantitative (CS, physics, math), though for entry level engineer positions, a bachelors is often enough. In terms of what to study, we look for a deep understanding of statistics, probability theory, optimization, and high performance computing. Some recommended books:

Hands-On Machine Learning

Deep Reinforcement Learning Hands-On

Deep Learning

The Elements of Statistical Learning

Machine Learning: A Probabilistic Perspective

Pattern Recognition and Machine Learning

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems https://www.amazon.com/dp/1492032646/ref=cm_sw_r_awdo_navT_a_NNXTVYE6KKA8QQSA076C

Career

I don’t know about courses, but this book gives a hands on approach… it has details in python, you should give it a try!

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

Hi there! Great question! First of all, Data Science has become a buzzword and an umbrella term. Data Science is a combination of 3 fields - Computer Science, Math and Statistics, and the Business Domain. If you use use scientific methods, math and statistics, specialized programming, advanced analytics, AI, and even storytelling to uncover and explain insights from data, I'd consider you a Data Scientist.

Topics to learn:

1. Programming (Python is extremely popular in DS)
   Python for Everybody Specialization (course)
   Automate the Boring Stuff with Python (book)
2. Math/Statistics (linear algebra, calculus, probability)
   Mathematics for Machine Learning Specialization (course)
   The Elements of Statistical Learning (book)
3. SQL
   SQL course on Udemy (course)
4. Pandas
   Kaggle - Learn Pandas (course)
5. Exploratory Data Analysis (EDA)
   Data Visualization with Python (course)
6. Machine Learning
   Machine Learning by Stanford on Coursera (course)
   DeepLearning.AI Specialization (course)
   fast.ai (course)
7. GitHub - to host your projects (more of that below)
   Git & GitHub - The Complete Git & GitHub Course (course)

Framework of learning:

1. Learn just enough to get you started
2. Do a project
3. Iterate
4. Be consistent/accountable

Practice makes it permanent! The best way to learn Data Science is by doing Data Science. And projects are a great way to learn faster, more deeply, retain information for a longer period, and showcase your skills. Learning is a never-ending process. I know this list might feel overwhelming because there's too much to learn. Break it down to small steps, learn something new everyday, and apply it to a personal project. It can be anything you're interested in! You will get a sense of accomplishment when you finish a project and will see amazing results in the long run.

Also, you don't have to wait to finish one topic to start the next one. I'd recommend learning different topics at the same time and applying them to your projects. Once you're done learning something, iterate over steps 1 and 2 by learning something new and starting a new project or improving the old one. It's a great way to stay excited and motivated throughout this journey. Once you're comfortable, you can start adding more topics.

Lastly, hold yourself accountable. Set deadlines to your learning process and be consistent. Don't try to learn everything at once or you might get overwhelmed and burnout. Accountability is key to doing anything and will be important to not give up.

Book recommendations/extra material:

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• Naked Statistics
• The Hundred-Page Machine Learning Book
• Practical Statistics for Data Scientists

This is not a perfect roadmap, there are tons of extra materials to learn. But hopefully I covered the basics to get you started. Data Science is HARD, so I'll finish this with a cliché motivational quote:

“The beginning is perhaps more difficult than anything else, but keep heart, it will turn out all right.” — Vincent van Gogh

Good luck :)

> hands on machine learning with scikit-learn and tensorflow

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

https://www.amazon.com/dp/1492032646/ref=cm_sw_r_cp_awdb_imm_8X2TJJG91YYMHG3FXQFQ?_encoding=UTF8&psc=1 this one is amazing. It's more than just DL, it also talks about pitfalls of ml in general and talks about classical ml like random forest.

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems https://www.amazon.co.uk/dp/1492032646/ref=cm_sw_r_cp_api_fabc_AA5Z7BN9X38FJ2YRJT5T

I have over 30 AI books, here are the top two for beginners :

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

https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/ - The second edition is in the works

Referral link free

For introductry ML material that doesn't delve deep into mathematics, I liked Aurelion Geron's Hands on Machine Learning book.

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

I do not use TF, but to learn PyTorch I have used official documentation in addition to this repo:

https://github.com/yunjey/pytorch-tutorial/

It is a bit outdated now, but still should be useful.

Finally, I really like how they combine mathematical explanation with practical use cases in Dive Into Deep Learning book. PyTorch and Tensorflow implementations should be available for almost all of the book, but some parts might still not have it because originally it was using MXnet.

https://d2l.ai/

This covers it pretty phenomenally. https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=asc_df_1492032646/

Among countless tutorials that can be found online one can certainly get lost.

Two books are usually offered as an introduction to machine learning:
1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
2. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

The good thing about the second book is that the lectures are available on YouTube:
1. Introduction to Machine Learning by Sebastian Raschka

Which of those books to choose, in my opinion, it doesn't really matter. Pick one and stick to your choice. Do the exercises and experiment with the code.

A follow up after you've finished one of the books above would definitely be Mathematics for Machine Learning.

Good luck.

I would highly recommend,

Hand's on Machine Learning with Scikit-Learn,Keras & TensorFlow

By Aurélien Géron

Links to buy: https://www.amazon.in/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646 (India)

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow-ebook/dp/B06XNKV5TS (USA)

Free PDF: https://www.pdfdrive.com/hands-on-machine-learning-with-scikit-learn-and-tensorflow-concepts-tools-and-techniques-to-build-intelligent-systems-e168440497.html

Thanks, I passed! The exam is very straightforward but challenging in it's own way. Below are the materials I recommend:

MANDATORY: https://www.tensorflow.org/extras/cert/Setting_Up_TF_Developer_Certificate_Exam.pdf?authuser=1#page=4&zoom=100,96,282 https://www.tensorflow.org/extras/cert/TF_Certificate_Candidate_Handbook.pdf

Should go without saying, but since this exam is so different to multiple choice certification exams, review the handbook and get comfortable in the environment and PyCharm first. Also, if you are using your own machine, set up a backup environment (collab notebooks are perfect). The last thing you want is Cuda problems on a problem (like I had).

MANDATORY: https://www.youtube.com/watch?v=rBwl50GAsvs

Of all the medium blogs/videos/pamphlets/etc. I read in anticipation, this talk by George Zoto is the most comprehensive, most digestible, and most relevant. He doesn't sugarcoat anything and describes what its like to take the exam well. George if you see this, thanks.

MANDATORY: https://www.coursera.org/professional-certificates/tensorflow-in-practice

Definitely take this class if you can afford it or have the time to do a one week free Coursera membership. I'm working full time and also in a masters in DS, but I was able to complete the specialization in about 10 business days (for time reference). If you take this class, go through the other materials I recommend, and have decent python3/ML knowledge, you can pass the certificate without anything else.

Optional (I have engaged in some capacity, but don't think you fully need to): https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646

Really great book and would recommend any ML practitioner has on hand. I've only read parts of the book, but from what I've read it goes beyond the level of depth needed for the test.

https://www.youtube.com/watch?v=5rSBPGGLkW0

This intro guide helped me a lot, but if you already know PyCharm or want to learn by getting your hands dirty, this guide is also not mandatory.

Happy to chat if anyone has questions, DM me or reply here.

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

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

^ might focus too much on machine learning aspect but its all encompassing as far as tensorflow techniques go

For a better understanding of machine learning I’d say read these two books simultaneously:

Introduction to Statistical Learning (ISLR): http://faculty.marshall.usc.edu/gareth-james/ISL/

Hands On Machine Learning with Keira’s and TensorFlow: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=pd_lpo_14_t_0/138-6162222-3173130?_encoding=UTF8&pd_rd_i=1492032646&pd_rd_r=23cef3ec-b579-44a1-b1d2-2695dc9fa6d7&pd_rd_w=VBE0y&pd_rd_wg=lahik&a...

If you’re comfortable with Python you should be able to complete the exercises in ISLR without touching R. If not, that’s where “Hands On ML” comes in. You can read both books at the same time, however, you should ONLY complete the exercises in the “Hands On...” book. When you get more comfortable creating models and using sklearn in Python you can go back and complete the exercises in the ISLR book.

Two resources for classification and regression. These are more than tutorials, but definitely enough to get you going where you can actually do something yourself.

Python: Hands-on ML

R: Intro to Statistical Learning

>Ps: O que é Big Data? É uma área em ciência dos dados, ou só um sinônimo pra isso?

Big Data basicamente mexe com 'dados grandes', no sentido de ser muita informação. Pense no número de posts do Facebook, no número de websites que o Google armazena, coisas de IoT, etc. O Big Data envolve tanto os processos para armazenar (data engineering) quanto para processar (data science) e analisar (data analytics).

>Gostaria de uma dica de vocês, por onde começar a estudar essa especialidade (quais cursos/sites/threads/livro),

Curso do Andrew Ng no Coursera sobre Machine Learning é incrível. O livro do Hands-on Machine Learning também é espetacular para iniciante. (https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_1?dchild=1&keywords=hands-on+machine+learning&qid=1598403532&sr=8-1)

>se existe mercado no Brasil, e se estudar por conta própria é possível?

Existe, quase toda semana eu vejo alguma oferta de emprego nesse ramo, apesar que são concentrados no sudeste. E, sim, é possível estudar sozinho. A maioria das pessoas fazem isso. Data Science no momento está vivendo um boom muito grande, só não sabemos até quando vai durar.

>Outra coisa, existe uma facilidade de eu conseguir um emprego como analista de dados fora do país (Europa/Canadá/EUA)?

Sim, mas o nível sobe muito e pode ser necessário instrução formal (por exemplo, mestrado/doutorado).

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Se você quiser aprender python, os packages a dominar: NumPy, Pandas/Dask e matplotlib são essenciais para começar. Aí vem os de ML como scikit-learn e tensorflow. Escrevi meio rápido porque o yakisoba acabou de chegar, mas qualquer dúvida deixa aí que daqui a pouco respondo.

Python e Julia são as linguagens mais utilizadas para Data Science. Acho que vi uma API tensorflow em JavaScript também, mas não utilizei. Os usos de C/C++ que vejo são mais para desenvolvimento ou quando se precisa implementar algum algoritmo que requer perfromance extrema.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition by Aurélien Géron

https://www.amazon.com/dp/1492032646/ref=cm_sw_r_sms_apa_i_89.qFb07HZWKN

Reinforcement learning seems to be the exact thing you are interested in. It is entirely centered on an artificial agent (e.g., a robot) trying to do something that it doesn't know how to do. It begins by trying out different things and sees what works and what doesn't. You would love this video as it shows what is possible with this type of learning: https://www.youtube.com/watch?v=Lu56xVlZ40M

You also mention other things like deep learning, dimension reduction(?), probabilistic modelling, etc. - these are not alternatives to reinforcement learning. They are modelling techniques you would use to implement a machine learning system, which includes reinforcement learning. I suggest you read an introductory book (example) on machine learning to get a good idea about the these modelling techniques and how they are used for the 3 major ML paradigms - supervised, unsupervised, and reinforcement learning.

And to answer your second question, reinforcement learning is a very important problem. There is no doubt about it. However, you are right in observing that (relatively) fewer groups are working on it than, say, on computer vision, which mostly relies on supervised learning. A big reason behind this is that supervised learning has had a lot of success in last few years and researchers are getting more bang for their buck by focusing on supervised learning techniques like computer vision. Reinforcement learning has had few successes too (e.g., alphago) but getting done something with supervised learning is still easier. This can of course change in the coming years.

Most researchers would suggest you to follow your interest instead of what is hot today.

Hands on Machine Learning is an excellent book.

3blue1brown has made an excellent video playlist that illustrates linear algebra in a very visual way. https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

I loved the book "Hands on Machine Learning with Scikit-Learn, Keras & Tensorflow" https://www.amazon.com/dp/1492032646/ref=cm_sw_r_cp_apa_i_XLHhFbE1YY8WF

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

If you are just looking for guidance on implementing them practically, this is widely considered a very good resource for that. You can probably find pdf versions online somewhere.

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

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

pra kaggle e tunning de modelo https://www.amazon.com/Approaching-Almost-Machine-Learning-Problem-ebook/dp/B089P13QHT

pra iniciante, recomendo assinar dataquest, ou data science academy, ou similares

edit: basicão pega na udemy por 30 conto

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=pd_sbs_14_t_0/145-2363324-8893158?_encoding=UTF8&pd_rd_i=1492032646&pd_rd_r=a3251da7-0f21-4638-a0df-d3d22ee181d4&pd_rd_w=0NWi9&pd_rd_wg=Nvs98&pf_rd_p=5cfcfe89-300f-47d2-b1ad-a4e27203a02a&pf_rd_r=6E3JPX73VMMWQYPHHMAS&psc=1&refRID=6E3JPX73VMMWQYPHHMAS

This is the new edition of the book I used - my version did not cover Keras. When I was experimenting with DNN I used Keras and had good results when attempting to replicate Inception-modules-like topology in 1D. I am not using deep learning models at the moment.