Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
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Books Computers & Technology Computer Science
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets.
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Aurélien Géron
Reddit Posts and Comments
0 posts • 49 mentions • top 42 shown below
43 points • ratterstinkle
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!
3 points • dataslinger12
Hands-On ML is the best applied pure machine learning book I’ve seen. It doesn’t focus on social sciences but lots of python coding is available and does a solid job explaining machine learning concepts and applications.
https://www.amazon.com/gp/aw/d/1492032646/ref=dp_ob_neva_mobile
3 points • ryendu
I recommend this book in machine learning. It was what got me started :)
2 points • bdalah
I think you should maybe start with the ML course first (both theory and practical). understanding how the theory and math behind several machine learning algorithms will make it easier for you to understand and TensorFlow applications better(with Neural networks)....that is what i have done and it has worked for me....
For a more structured learning of the whole ML field(both theory and practical), you should checkout this book. its amazing. Goodluck
4 points • country_dev
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.
1 points • bageldevourer
I came from a similar background as you. Before buying a book, I'd recommend going through the scikit-learn user guide. Once you implement a few models there, you'll get the picture; the API is very consistent and well-designed.
For DL, again, I'd go to the user guide for either TF or PyTorch.
At some point you'll notice that scientific programming in Python is basically impossible without NumPy, so you'll need to get good at that too.
​
If you absolutely need a book, this is the guy for you.
1 points • syrios12
I made the transition from economics to data science. You definitely have a lot of the skills necessary already. I'd suggest picking up a copy of "Hands-on Machine Learning" to get more of the machine learning and Python skills and then do a lot of networking on LinkedIn to help get your foot in the door. I also have created a course that's an introduction to Python and analytics. If you're interested, let me know and I can send you the link. Best of luck!
3 points • COMPSEDIT
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
- 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.
3 points • rimpakcha
For NLP in Python, read: Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems
For Python, check out the freeCodeCamp Python videos on youtube
For ML in Python read: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
1 points • DiogenicOrder
Géron's book is great and has a chapter about clustering so I think you might want to look in there.
If you already know the clusters and which conditions fall into which categories, you could simply do this as a classification. This will be up to domain knowledge and what best answers your research question so I would ask someone senior or check the literature on that.
Good luck!
1 points • math_SS
Or to have a medium to work in industry then I think Geron's book is second to none.
1 points • TaryTarp
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
1 points • globalminima
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.
1 points • sachinchaturvedi93
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.
1 points • misterforsa
I highly recommend this book as a starting point. You get hands on with some of the main tools, without too much of the heavy duty theory. Given the range of knowledge required for the job, it's best to start with something like this. Get hands on experience, then it will help you get into the heavy math and theory later on.
https://www.amazon.com/gp/aw/d/1492032646?pf_rd_p=a0de36ed-fbca-4567-b355-7a021be5a3fe&aaxitk=Cez2.d860RCp2GL2B8hnEg&hsa_cr_id=5407701540001&ref_=sbx_be_s_sparkle_scm_asin_0
1 points • gevezex
If you learn better from a book:
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
And if you learn better with video I would suggest fast.ai and the especially free course: Deep learning for coders and the part2 (total of 14 videos).
The last one will give you a very good understanding and intuition about the deep learning processes with state of the art results with a top down learning approach what suited me very wel.
The book is ultimately based on scikit-learn librabry and tensorflow and the video course is based on pytorch and fastai libraries.
For finance purposes there is not much resources what I know. You probably need to find segmented medium articles to get some knowledge.
1 points • cryptohacker
I hightly recommend Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems.
I started reading this book last month. The author dives deep into an ML project first, which helped me to understand how an ML project work from end-to-end. He introduces the functions and explains the math in the upcoming chapters. So far, I love this book and I am determined to finish it.
1 points • BigTheory88
Self learning works just fine if it's something you'll be dedicated to.
Data science in computing used to be focused on statistical methods (and still is) but there has been a shift in recent years to machine learning methods. To get started you'll need a grounding in mathematics. You'll need to know some statistics/probability theory, calculus and linear algebra.
Here's some resouces: https://www.amazon.com/gp/product/1491957662 https://www.amazon.com/gp/product/1492032646
5 points • howardbandy
I am a retired professor of Computer Science, and a practitioner of deep learning.
Here are two excellent books:
https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/
Check YouTube for video lectures. Those by Google are a good place to start. Lawrence Moroney is an excellent lecturer.
1 points • Dam_uel
This covers it pretty phenomenally. https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=asc_df_1492032646/
1 points • birl_ds
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
1 points • Reading102
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
1 points • colonel_raptor
I completely agree. I took an ML course in college and had ML work come my way. Applying ML without DEEP math isn't too bad - just look at Hands on ML with SciKitLearn, TensorFlow and Keras. This is all about utilizing algorithms that have already been built for you. Same thing with this Coursera course by Andrew Ng. I'm not an ML engineer - I just use this stuff on the job based on what I've been taught in these books. Not that hard without an ML background, just need a really solid programming background to do it well.
1 points • raijenki
>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).
​
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.
1 points • sinusgamma
For deep learning, I think this is awesome: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646
3 points • golf_wolf_1
u/vastlik's recommendations are good. That said, each of those books is big and they don't really have much by way of coding exercises as far as I know.
If you want to get your hands dirty then I think Geron's book is second to none. Its coverage is broad, ranging from bread-and-butter methods (e.g. regularized regression, support vector machines) to more ``advanced'' topics such as deep learning and reinforcement learning, and it includes exercises and examples using python, which is a pretty easy language to pick up. There's just enough math to keep you engaged (linear algebra, statistics) and it's clearly tied to using it in practice.
A theoretical exploration of machine learning can send you down a real rabbit hole, or really a warren of interconnected rabbit holes. The reason for this is that the field exists at an intersection of math, statistics, and computer science. As such, each field brings its own perspective to understanding machine learning problems. This book is frequently used for ML theory classes and it does a good job, in my opinion, of formalizing the problem from a computer scientist's perspective. Specifically, you want to know if you can, with high probability, efficiently and accurately approximate a function in a class, where accuracy is measured by minimizing some loss function. Already we have three areas of math/cs/statistics: probability, computational complexity (efficiency), and optimization (minimizing the loss function). This book is similar in rigor and scope but with a more thorough set of appendices.
Efron and Hastie have a book that might form a sort of middle way between the two suggestions above. It situates modern ML historically in the developments of statistics and computation and gives mathematical glosses of some common and not-so-common topics .
Let me close by suggesting another way into the theory, since you said you miss doing math. Pick something applied that you find interesting and start digging backwards from there asking ``why does this work?" This is likely going to keep you motivated longer and I would say you're nearly guaranteed to run into interesting mathematics.
If you pick image analysis, for example, you can quickly find yourself reading about functional analysis, symmetry groups, and fourier and wavelet transforms. Maybe you say to yourself "it's cool how we can analyze natural language" and you'll be into probabilistic context-free grammars, graphical models, and recurrent neural networks. Perhaps you're an MLG gamer and want to know how AlphaGo works. Now you're into markov processes, optimal control, function approximation, game theory, and measure concentration. Pretty much all applications have been addressed by deep learning with various degrees of success. Regrettably the how of deep learning is fairly simple, but the why of deep learning is very poorly understood. The best tool for analyzing their properties, as far as I know, is the Neural Tangent Kernel which is two years old.
If you don't know any application areas, maybe start with Geron to get a sense of them and explore from there.
1 points • OSUOnlineMLClub
Sure thing!
If you’re like me and you learn best through experience, then I would highly recommend picking up a copy of this book:
https://www.amazon.com/gp/aw/d/1492032646/ref=dp_ob_neva_mobile
It walks you through the core concepts and gives you a lot of material to practice with. I’m reading through it right now and it’s really fantastic.
If you’re looking for videos, and maybe more interested in the math around ML, I would recommend also going through the original ML course by Andrew Ng. You can find the videos for free here:
https://m.youtube.com/watch?v=PPLop4L2eGk
I’ve heard some people say that it’s a little dated, and it doesn’t use Python, but it’s a good walkthrough of the concepts in video form.
If you’re especially interested in the math of it all, then I would also recommend going through the first few sections in Gilbert Strang’s Linear Algebra course on MIT OpenCourseWare. Although if you’re not interested in ML for advanced/research purposes, you could probably skip that. I just watched because I like math, and it definitely does help if you want to go deeper than surface level.
Honestly there’s about a thousand ways to get started with ML, and there are much more thorough write-ups about it than this one out there, but the mistake I think a lot of people make is that they spend too much time trying to figure out where to start instead of just jumping in and getting their hands dirty.
With all that being said, I say just go for it! I wish you the best of luck in your new studies.
1 points • srspore
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
2 points • sinefine
I am in the same situation as you. I went through Andrew Ng's course but didn't feel confident. I tried taking this but holy... it was difficult to understand. https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU
So instead I followed this book: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646
The book has a Google Collab that goes with it https://colab.research.google.com/github/ageron/handson-ml2/blob/master/
I didn't bother trying to really understand all the internals of everything like math and theory because I think I will eventually get it as I use them over and over. I just wanted to know how to use Keras and Tensorflow. I'm way more comfortable now, but I will practice more. I just finished chapter 17 and just decided to stop there since Reinforcement Learning (chapter 18) isn't something I felt like would be useful at a potential job opportunity.
If you do want to learn Reinforcement Learning, this video is better than the ch 18 of the book imo. https://www.youtube.com/watch?v=ggqnxyjaKe4&feature=youtu.be&t=935
Hope this helps.
2 points • bigfuds
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.
1 points • Rebbit_and_birb
First of all, you don't state which graphics card(s) (GPU) you have in your computer (I assume you have a graphics card, if not, there is your solution). It is recommended to have a gpu with minimum cuda capabilities of 3.5 for TensorFlow. Afaik you can work around that but I assume you'd have to install cuda and cudnn yourself and not just conda install tf-gpu (although i haven't tried running it on a card under 3.5). I personally would recommend installing tf-gpu with pip like in the official guide (that's how I always do it anyway).
Secondly, I presonally would recommend doing ML in Linux (for example ubuntu 20.04) because it is more modular and easier to control installing things just the way you want.
And finally, since i am assuming you are rather new to ML and TensorFlow, the types of projects you will start out doing won't be that computationally expensive. Sure, it will be slower but it'll take a minute to run the model instead of ten seconds. Gpus only really become important once you're working with big Neural Nets where the difference is a day versus a month.
If I were you I would just try to get TF2.0 running without worrying about the gpu for now and learn the basics. I did my whole first semester in ML on a macbook pro from 2011. Google has good tutorials or free and I highly recommend this book.
​
I hope this helps
1 points • pseddit
That course is very good if you want a quick review of Linear Algebra and an understanding of gradient descent. It has two problems:
- It’s content only partially lines up with CS7641.
- Matlab and Octave is not commonly used and puts unnecessary burden of learning on the student
If you are comfortable learning from books, these two will put you on a sound footing.
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_cp_api_i_c9KyEb8EXPCH7
1 points • snnlaat
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.
1 points • zrcqn
Tensorflow’s website is a good start. They have some pretty good documentation for both beginners and experts. I’m not sure what your proficiency level is with ML, previous versions of TF, or programming or what you’re hoping to do exactly, but I’d also recommend this book by Aurélien Géron whose also got plenty of code to accompany the book available here. I also remember referencing this book a few times. I had to learn TF 2.0 on the job and these books were really helpful.
1 points • fkdosilovic
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.
2 points • BerkshireHathaway-
Probably the best intro course out there
This is also a decent course from UMich
I read this, this, this, this, and this and would recommened them all.
1 points • mr__n0b0dy
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.
1 points • JustAnotherEngineer_
Its not from a book, its from some lectures I purchased at Udemy: https://www.udemy.com/share/101WyWBkocdF5aRH4=/
(1 free lecture can be found here too: https://www.udemy.com/share/102RsKBkocdF5aRH4=/
I also purchased a few others that I'll be looking into: https://www.udemy.com/share/101ZdoBkocdF5aRH4=/ https://www.udemy.com/share/101ZRuBkocdF5aRH4=/
The lectures from the first link are alright I guess, I haven't looked into the 2 other lectures, but they look promising. The lectures can be pretty expensive, but I also did some research online and after digging for some books I found these two that I will start reading, so if you can't afford the lectures maybe these books will help: https://www.amazon.com/gp/product/1492032646/ref=ppx_yo_dt_b_asin_title_o00_s00?ie=UTF8&psc=1 https://www.amazon.com/gp/product/1617294438?pf_rd_r=G5TYQ0J1AFC5G1FNHZ7P&pf_rd_p=edaba0ee-c2fe-4124-9f5d-b31d6b1bfbee
Hope you find this information useful!
1 points • permalip
First of all, expect it to take longer than a month. Just being realistic here.
1) Linear Algebra
It's very much recommended to work on your Linear Algebra understanding first! The simplest and best way to learn this is 3Blue1Brown: Essentials of Linear Algebra, he is simply amazing at explaining the concepts.
2) Statistics / Machine Learning
Next, a solid start to statistics/ML is An Introduction To Statistical Learning, which will teach you many of the fundamentals. Next, you can go for a more practical book like Hands-On Machine Learning.
3) Neural Networks
If you want to learn about neural networks, I have a series of articles that introduce you:
- Neural Networks: Feedforward and Backpropagation Explained
- Optimizers Explained
- Activation Functions Explained.
Perhaps the best book on neural networks is Neural Networks and Deep Learning by Michael Nielsen. But once again, 3Blue1Brown also has an amazing Neural Networks series.
If you work your way through all of this material, I believe you have a solid starting point! But, as always, there will always be more to learn. It's a steep learning curve at the start but try to stick with it.