Everything Data Science

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In this video I will give you the resources you need to learn data science from zero knowledge. We will discuss several programming books and math books that are perfect for beginners who want to acquire the skills to become a data scientist. In particular we will look at books on R, Python, Calculus, Linear Algebra, and Statistics. Several more advanced books are also presented in this video. Do you have any other book recommendations for learning Data Science? If so, please leave a comment below.
Programming Books
The Art of R Programming amzn.to/3GqxS59
Larning Python amzn.to/3EGRGjd
Python Crash Course amzn.to/3tDlR4z
Doing Math With Python amzn.to/3TGdAra
Calculus Books
Calculus by Stewart amzn.to/3V3rCnQ
Calculus by Larson amzn.to/3XgjHp1
Calculus Early Transcendentals by Briggs amzn.to/3V1qfGa
Vector Calculus amzn.to/3UEUhjj
Linear Algebra Books
Elementary Linear Algebra by Anton amzn.to/3tDbrBQ
Elementary Linear Algebra by Larson amzn.to/3GsjtoW
Introduction to Linear Algebra by Strang amzn.to/3TMHU3v
Linear Algebra by Wilde amzn.to/3THlmkt
Elementary Linear Algebra by Grossman amzn.to/3hSLAn5
Schaum's Outline of Linear Algebra amzn.to/3TMzwAQ
Linear Algebra Theory and Applications by Cheney and Kincaid amzn.to/3gcRfnA
Linear Algebra by Friedber, Insel, and Spence amzn.to/3V1s6L8
Statistics Books for Beginners
Understanding Statistics by Mendenhall amzn.to/3EJ2SMu
Probability and Statistics for Engineers and Scientists by Ross amzn.to/3XekCqc
Statistics by McClave amzn.to/3GskLjM or amzn.to/3UTLoCM
Schaum's Outline of Probability and Statistics amzn.to/3XeJTQO
Mathematical Statistics Books
Mathematical Statistics with Applications by Wackerly, et al. amzn.to/3EaX5xq
John Freund's Mathematical Statistics with Applications amzn.to/3UX0bvY
Advanced/Specialty Statistics Books
The Statistical Analysis of Experimental Data amzn.to/3ANaaMT
Design and Analysis of Experiments amzn.to/3TGiCE4
Applied Regression Analysis amzn.to/3ElFCT0
Methods of Multivariate Analysis amzn.to/3UN4QRs
Nonparametric Statistical Methods amzn.to/3OiArrz
Applied Linear Statistical Models amzn.to/3EHpIE5
Introduction to Linear Regression Analysis amzn.to/3UX1Vp0
Probability and Statistical Inference amzn.to/3Oon5Kz
Statistical Methods amzn.to/3EGxaPK
Applied Multivariate Statistical Analysis amzn.to/3tDrOhV
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27 сен 2024

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Комментарии : 259

@slhermit 10 месяцев назад

Before doing all these studies, pick a field where you would like to work as a data scientist. That simplifies a lot of things. For instance, you can only focus on learning specific statistical methods, learn about data in your chosen field, master one programming language + SQL, and learn about cloud computing basics. Let s say that you like to be a data scientist in finance. Then learn statistics relevant to finance, SAS, advanced Excel, SQL. Let's say that you like to be a data scientist in biotech. Then you need a biotech-related PhD + cloud computing + R or Python programming, SQL Let's say that you like to be data scientist focused on Analytics (like most META hiring), you need to learn very basic statistics, SQL, knowledge about products by that particular company. Once you find a job, Keep learning, research on real-world problem solving.

@mehdilee 6 месяцев назад

Thanks!

@aiiishiba 4 месяца назад

What if I want to use data science for sports analytics, how should I go about that?

@chase7343 Год назад

Doing a theoretical math BS with a minor in comp sci and I'm heavily considering data science for grad school. The perfect video to watch!

@n.ganadily8973 Год назад

A Master in Data Science or a Masters in Statistics will help you a lot in finding a Data Scientist or Machine Learning Engineer Job

@owenjackson4751 Год назад

Im doing a BS in comp sci with a minor in math and Im thinking the same thing

@cesarcardoso4265 Год назад

Hey! I did exactly the same combination as you in undergrad. I also went on to do a DS MS and it was totally the right decision for me. I recommend you start digging into some stats courses if you can before making the decision. Personally I finished my Math BS having taken 0 stats classes because "that wasn't REAL math". Big mistake. If I could go back in time I would've taken a lot more stats and a lot less topology and number theory.

@RyanOManchester Год назад

I read this as "Doing some theoretical math BS" which is a bit different than doing "a theoretical math BS." I highly recommend introduction to machine learning by Alpaydin btw. Helped quite a lot in my first year of grad school

@chase7343 Год назад

@@RyanOManchester lmaooo. One could argue those aren't too far apart

@federicosilva8849 Год назад

Great resources! I loved the fact that you included textbooks that require proofs. For me, the type of math taught at engineering schools is what I'd call (in an analogy to software testing) "black-box math": you know how to do computations, you know what the theorems are used for, but you don't get to see the "code", the logical structure that makes all these theorems actually true. I prefer "white-box math", even if it's a lot harder, it's a lot more rewarding at the end of the day, and you end up having a more profound understanding of how and why things work.

@leusmaximusx 9 месяцев назад

these white-box math must be taught in masteral classes, we use it in validating/challenging the calculations done by the professional engineers whether they understand the suitability of their formulas for the job , more often the products are overdesigned and super expensive , im sick of engineers making legacy projects at the company's expense

@stevenkies802 Год назад

Doing a Data Science boot-camp to follow up a Cognitive Science Ph.D. This is a great resourse. Thanks!

@sankiago Год назад

WOOOW amazing PhD bro

@PavloFesenko Год назад

I also wanted to recommend "Introduction to the New Statistics: Estimation, Open Science, and Beyond" by Prof. Geoff Cumming (2nd edition coming in 2023). Prof. Cumming really explains very well the predominant importance of confidence intervals and effect sizes as opposed to only null hypothesis significance testing. 😉

@jamesedward9306 Год назад

Thank you Sorcerer for your continued prodigious output of Mathematics information sources. You are a truly valuable resource for those of us trying to learn this stuff on our own. Your enthusiasm is infectious. Well done and Kudos.

@johnwig285 Год назад

@naydoorf Pretending? Yeah and he also makes money off the views for every video too so what? You're clearly lacking common sense. Making money off something equates to pretending nowadays apparently. You act as if he recommended Harry Potter books for a data science topic

@mkwarlock 7 месяцев назад

Great stuff! I'm currently pursuing a master's degree in data science, and learning a ton of mathematics. This semester I'm enrolled in linear algebra, applied statistics, and graph theory.

@SimonSolves_Math Год назад

Data scientist is one of the careers I’m looking to become, but I’m also interested in becoming a mathematician or math professor. Thanks for the books!

@declanfarber Год назад

Oh, so you’re okay. You got us going there. That’s kind of weird. I don’t know if I’m going to follow this channel anymore. Good luck with all that drama.

@mr.fantastic7756 Год назад

@@declanfarber what do you mean?

@rusi6219 Год назад

@@declanfarber same here

@declanfarber Год назад

@@rusi6219 Some stuff got moved, edited or deleted. Pay no mind then, it is to talk into the aether.

@nails7534 Год назад

Highly recommend Introduction to Statistical Learning by James, Witten and Hastie. It is a clear and thorough exposition of the bias variance tradeoff as well as a variety of common models.

@aflyingtoaster6096 Год назад

It's my first year in university studying data science, and i can tell u this the best video that explains what are the "must know" for a data scientist, and also i appreciate all the books review videos , they're just amazing

@hellfishii Год назад

As a Data Science undergrad I can say this is a fantastic and comprehesive overview of the matterial we study, great stuff! keep it up!

@thepersonperson4332 Год назад

Python and R (Programming Languages) 0:43 Calculus 2:00 Linear Algebra 3:36 Statistics 6:56 Specialized Books 9:52

@slhermit 10 месяцев назад

@leusmaximusx 9 месяцев назад

@@slhermit i want to create engineering heurictics for energy systems to predict results, is that doable through data science math ? context : im not an engineer but a budget officer , want to assert that a building with excessive quantity slender columns and unnecessary expense and 80% chance of not surviving seismic level 7 , without doing/reading horendous calculations by opportunistic engineers

@bargainbincatgirl6698 Год назад

Some other recommendations to add, but it could be a little over the top, are books of machine learning. Pretty good free books, that can be downloaded free (and legally) are "Elements of Statistical Learning" of Hastie, Tibshirani and Friedman: this one requires advanced mathematics, similar to the requirements you mentioned for the Mathematical Statistics book, and is THE book to learn machine learning. But there are other book of the same authors, "An Introduction to Statistical Learning" of Witten, James, Tibshirani and Hastie that tries to be more about application of machine learning with less mathematical deep. Still is really good

@JesseMaurais Год назад

My only addition would be a book on design patterns for software development. It helps particularly when you are going to be working in the same code for a long period of time or with a larger team of people. But the choice of book here is going to depend on the language you are working in. Otherwise great picks. The stats book with Mendenhall, Wackerly, and Schaeffer is also my first reference book.

@Anonymous-qw Год назад

It is amazing how many of the textbooks I used for my Mathematics Bsc(Hons) from the 1980s are still used. I used both the Gilbert Strang Linear Algebra with Applications and the Seymour Lipschultz Schaum outline Linear Algebra. For Probability and Statistics I used Introductory Probability and Statistical Applications by Paul L Meyer and Introduction to the Theory of Statistics by Alexander M Mood, Franklin A Graybill and Duane C Boes. Although not my favourite at university, because I worked in the Banking industry afterwards Statistics proved to be one of the more useful subjects I learned at university.

@lukealexander4512 Год назад

Great video! The following information that I will provide should be taken with a grain of salt. If a person is a college student, one beneficial major that could lead to a future career in Data Science is Computer Science (CS) with a minor in Statistics. When comparing a Computer Science Degree with a Data Science Degree, the coursework is fairly similar. As an undergraduate, I prefer a Computer Science Degree as it emphasizes programming concepts that will help when applying for jobs as a majority of Data Science jobs require a Master's Degree for candidacy, while CS jobs mostly require a Bachelor's Degree. Additionally, a minor in Statistics would allow students to cover the statistical concepts mentioned in this video.

@nickhill6036 Год назад

Norm Matloffs book on R is excellent. I use both R and Python. I'd say that for all things stats related, I use R. I tend to use python for a lot of data pipelining and nlp. The statistics procedures in python tend to be problematic. I don't believe in language wars though. I use C/C++/fortran within r and python to speed up stuff as needed as well. I also keep SAS guides handy. They are excellent for understanding procedures and have paper references. It's fallen out of favor though. 'Design and analysis of experiments' is good to have. Great job including it. Not many people understand that topic.or the need for it. I'd recommend knowing hierarchical modeling as well. Gelman and Hills book is one I would highly recommend. Last thing I would like to make clear is that you would be a good data scientist if you don't think with your tools/math first. Tools are tools. Your job is to solve problems. In most cases, the reason for your job is to enable the employer to make or save money. Many data scientists think their job is an extension of grad school. So, they want to use the latest and greatest algorithm they read about. Great minds. But highly ineffective, who end up wasting their and everyone else's time. Putting things into use in a running machine like a complex business, is hard in itself. The more complicated your solution, the longer it will take to make it useful, it will be expensive to maintain, will need constant supervision, and leave everyone exhausted and exasperated. This is not trivial. Data Science courses popping up produce unusable talent because it's taught by people who have never done any real work.

@sophiaisabelle01 Год назад

Data science seems interesting to learn. May you continue to receive more blessings along the way.

@ANTGPRO Год назад

God bless you.

@aniltraveldiary Год назад

Man I used to watch your videos some years ago when I was doing my bachelor's degree in statistics! Your videos helped me more than the lectures from my teacher. To be honest, I completely forgot about the channel and now I'm learning about data science and your video popped up again! Thank you for all those efforts your videos are so clear and easy to understand. Love from Nepal

@MrHaggyy Год назад

As you mentioned references quite a few times in this video. In data science/engineering it is so damn important to be precise to the letter about what assumptions, tools, methods, and state of data you used when, and why. Clean work is always important in any science or engineering topic, but in applied work with data, it is so easy to be off significantly. And expressions that clearly show you that you are wrong like the lagrangian in mechanics are rare. In Europe we use the Lothar Papula quite a lot for math, I learned python by extending the basics, tutorials, and examples from the documentation. Now I`m working with "Data-Driven Science and Engineering" by Brunton & Kutz as well as "Dynamic Data Analysis: Modeling Data with Differential Equations". It's quite tailored to control theory and system engineering so might not be the best "Data Science" book but it`s great if you want to build robots, machines, etc.

@Drganguli 24 дня назад

Most data scientists can use python and pandas along with PyTorch to do the math needed. The key skill in data science is how to get the data and figure out what you can do with the data.

@john_paul Год назад

Must have read my mind! Just started a new job in programming and trying to skill up a bit more while finishing my onboarding tasks. :D

@saisreekar4425 Год назад

hi math sorcerer i'm using R for projects and there's a book that i'm using called R for everyone by jared lander which he explains R from a beginner level and i'm happy that you've included a book on R which plays a crucial role in data science.

@AskMeMaths-m7q 3 месяца назад

Programming: 0:41 Calculus: 1:59 Linear Algebra: 3:36 Statistics: 6:31

@jorgegarza23 Год назад

Thank you teacher, next week it's my birthday, i'm currently studying Data Science in University and this video it's a great fount of inspiration!

@colefees139 6 месяцев назад

Watching this while procrastinating on studying for my data science exam

@thoughtsbright7928 Год назад

I actually want to be a machine learning engineer and great at mathematics this video the best thank you

@cs-atulkumartripathi4865 2 месяца назад

Please make a recent video as keep in mind all aspects as GenAI , NLP ,CV ,DL ,ML, PYTHON , STATISTICS, MATHEMATICS and GUESTIMATES

@musonobari2560 Год назад

A Whiff never misses a Math Sorcerer's book review 😂😂😂😂😂

@officialcommentcheckerofth9703 Год назад

A very much needed video, I'm currently a freshmen in Applied Mathematics with my interests lying in AI/ML/DL.

@richardpointer Год назад

That Schaum's Outline to Linear Algebra moved me from a B- to an A in my first Linear Algebra course. It was like turning on a lightbulb.

@techtodas1169 5 месяцев назад

Wow! Didn't know you have actually video about data science which I'm studying right now. You're cool!

@eolle43 Год назад

Good selections. I would also recommend one on Bayesian inference (ET Jaynes Probability Theory is good),and graphic display of information (Tufte's books). There are also several good books with code outlines for basic Machine Learning / AI algorithms.

@hopelesssuprem1867 Год назад

It's a good idea showing materials to get prepared to data science and machine learning. In my opinion this is a new step in math evolution.

@AaVictor Год назад

Ultimately, calculus serves as a big foundation and prerequisite. As for all science.

@zedrengar1030 Год назад

Thank you so much Math Sorcerer you are a life saver !

@normangoldstuck8107 5 месяцев назад

I loved math at school and taught myself a bit more as a medical student. I would like to learn enough to understand modern physics including quantum theory and general relativity. This is one of the few channels I watch which does not have irritating music in the background. I hope you are not considering it?

@brianmccormick8328 Год назад

Going to have to say that Python is the better language to take up. Used both R and Python in a statistical machine learning course and it was extremely difficult to understand the R examples, while it was very easy to understand the Python examples.

@stevenkies802 Год назад

Python is definately the more understandable and flexible language. R is falling out of fashion, though it is computationally more powerful.

@stretch8390 Год назад

R, python, julia, etc., they all have strengths and weaknesses depending on your use case so I think you gotta give terms and conditions when recommending languages lest you lead people astray. R has an enormous advantage in biomedical packages thanks to Bioconductor. It also leads in niche economic analysis where a lot of the academics use R. Python is more prevalent in industry and is a fabulous general scripting language.

@dhickey5919 Год назад

Dude! Holy Smokes! I'm going to need another room to build a second library!

@MiketheCoder Год назад

Intro to statistical learning in R

@reganmian 10 месяцев назад

I love "Mathematical Statistics with Applications", but I'd suggest a more rigorous book like "Introduction to Probability" for the first half. Blitzsteins STAT 110 lectures from Harvard (what the book is based off of) are on RU-vid. It'd more digestible than "Statistical Inference" for self study, but covers the first half of the material and then some very well

@felipegud951 2 месяца назад

Here in brazil we have used stewart for the past 15 years as well

@chocolatecornetnothermitcr6159 Год назад

I study mathematics and a little interested in data science but have never learned it because it requires programming skills and knowledge beyond that of mathematics I’ve learned so far. Though I’ve already learned fundamental statistics and probability theory using measure theory, they seem to be not enough

@АлександрПетрунин-о7с Год назад

Nice review! Smell is important, I'll take it into consideration. Thank you!

@boyangirginov6043 Год назад

Man, awesome content, keep it up, Sir!

@TheMathSorcerer Год назад

Much appreciated!

@devd_rx Год назад

I used the statistics book by Wackerly, Mendenhall.. for my statistical mechanics course, they were indeed very helpful

@tanaypatel8412 Год назад

I like your pfp, quite a unique choice of putting plain blank space as pfp.

@snorky4506 Год назад

I'd like to suggest Statistical Distributions by Hastings and Peacock.

@ankitacharya5307 Год назад

thank you

@davidsoto4394 Год назад

Excellent video. This has been one of your best. Please do a video like this one about mathematical modelling.

@TheMathSorcerer Год назад

Great suggestion!

@davidsoto4394 Год назад

Please do seperate book reviews on every single book you showed in this video.

@peterp79 10 месяцев назад

I LOVE THIS VIDEO!! Juat what I needed.

@i_youtube_ Год назад

Guys if you want to die quickly in your learning journey to become a data scientist then waste your time in math books. Learning math and spending long time in it is something wrong and waste of time. You should learn Python then learn fundamentals of data science and machine learning by writing python code. You cannot understand data science and machine learning without writing code. After solving some problems in python, you will understand what is needed for this field and you will know which statistics and math topics you need to learn or relearn. Don't waste your time in math before practicing data science and machine learning in Python otherwise you will find yourself lost in a lot of math without any context. You will enjoy math in programming and you don't need advanced math for most of problems.

@robertocaropenaloza7764 Год назад

Lord Sorcerer, do you know some books or paper about: -Numerical Method for Variational calculus. -Nonlinear programing. -Numerical nonconvex optimization. -FEM optimization ( reduce computer load, maybe Dim. reduction)

@miguelcampos867 Год назад

This video is fantastic! Thank you for this advice. Would you recommend some books for those who are machine learning scientist? In my case, I am studying for a Ph.D. in Machine Learning (Deep Learning), and I have noted some lack of math when I am reading papers. I see everywhere that introductory algebra, calculus, and statistics are needed. But that is not how I see it. I would like your opinion and if you could make a similar video recommending some books. Thanks

@musonobari2560 Год назад

These book reviews are awesome man. Thankyou for that 😀👌🏽👌🏽👍🏽

@SRIRUPSARKAR Год назад

I am from India Thank you 😊

@phanisuripeddi7688 8 месяцев назад

While I understand the value of Algebra ( as we encounter some concepts like eigenvector , a concept in Matrix theory) , Stats ( for sure) what I really don’t understand is the significance of calculus in data science. Can someone make an exclusive video on this the applicability of differential and integral calculus in general?

@n.ganadily8973 Год назад

Video Suggestion: Everything for Theoretical Physicist (All of Physics)

@CD6GLL 4 месяца назад

gracias por el consejo...

@poteimiameso4455 Год назад

I suggest adding optimization and high-dimension data analysis to the statistics stack.

@MrEgor31 Год назад

Smell the page flipping through the screen, thank you Maestro

@John14vs6_ Год назад

I wish I could double or triple like this video. Thank you sir.

@ShahZ Год назад

Thanks @TMS, so much to learn so little time.

@nightowl32 Год назад

awesome plethora of books.....hey buddy...btw....I just passed the clep college algebra exam....thx a mil!

@TheMathSorcerer Год назад

That is awesome!

@nightowl32 Год назад

@@TheMathSorcerer you were in my ear the whole time.....lol!

@arashparsa4012 18 дней назад

@@TheMathSorcerer do you have anything for Bayesian statistics? I’m in a Georgia tech machine learning program - can reach you over email for questions I have for you.

@khaledfarrag9754 Год назад

thank you for your efforts

@TheNaldiin Год назад

I like physical books and have a prodigious Kindle Library, but how do you feel about the subscription services like Packt, O'Reilly, or Scribd that offer lots of digital materials for study?

@surrealistidealist Год назад

I was told that a deep understanding of mathematics isn't necessary anymore, because computer programs can do all of that work for us. But I never believed that. My main interest is social science research, and as far as I can tell, there's still a big replication crisis in the field. I'm pretty sure that a deeper understanding of mathematical statistics and related areas of math will be a vital part of the solution.

@Anonymous-qw Год назад

One my colleagues wife is a Social Science lecturer at a university. She said that one of the main problems with the students they get is they are unable to do Maths.

@surrealistidealist Год назад

@@Anonymous-qw I've always felt that was the main problem in social science in general. Too many people go into the field with poor math skills, and they usually don't bother to improve.

@rusi6219 Год назад

@@surrealistidealist they go into that field because they're not forced to do math by it. It's not a they problem, it's the field itself that's the problem.

@moserboser Год назад

Thank you so much for this video!!

@troybird8253 Год назад

You just need to figure out how to utilize a microchip and the code references related to the voltage and expectation or what frequency band do you attempt to influence while utilizing the statistical data you are looking for or require for a specific outcome you are seeking.

@Katie-hj5eb Год назад

Yep checked my bookshelf and the Calculus book is by James Stewart. Interesting its mostly the same everywhere.

@anhnguyenhong8770 Год назад

Real-time investing required me to calculate accurately when to cut-loss, Take-profit, break-even. I worked so hard and smartly.

@roni1451 Год назад

Wow, nice list. I've been looking into Computational Mathematics(Physics). Those requirements are a bit different

@patrickvassallo2884 Год назад

Great selection of books!!!

@TheMathSorcerer Год назад

Thank you!

@armir6063 4 месяца назад

But whats the point of studying all of it ? We have python to make all the calculations

@vinnymirando5929 Год назад

If you wanna learn programming. Dont read a book. Get an idea and try and code it. Use interactive courses

@hamzasehavdic Год назад

Youre our Math Godfather

@johndoe-id2uh 3 месяца назад

Thank you 🥹

@bwbs7410 Год назад

Should we read ALL the books you showed? Or which should we pick?

@valentinrafael9201 4 месяца назад

So what are some books if you don't have unlimited money?

@athicp.5111 Год назад

Thank you so much

@jessicagomez1760 Год назад

I had basic stats, cal and algebra in my business bach., Now that I want to persue a data science journey the intermediate and advanced books will definitely help a lot. I laughed so hard when you could not help yourself and sniffed the books ✨🥸☕👌🏻 man of culture

@federicodibernardo2719 Год назад

I studied all these things in computer science university with excellent grades, now 10 years later I remember absolutely nothing :(

@Accanfo 22 дня назад

Super

@pushkarnagpure2357 Год назад

Great review...can u do a rsview of g.hadleys books?

@sipsip2367 Год назад

thank you for making this video

@srinjoykar7236 Год назад

Please do it for QUANTITATIVE FINANCE

@theodrake2394 Год назад

What is the main difference between a data scientist and software engineer? Is there a lot of overlap. I’m a little bit torn and what to take in school. Any input would be appreciated

@AnonymousBosch3158 Месяц назад

I love you so much!

@patinho5589 Год назад

In practice I see companies recruiting data scientists when the companies are nowhere near ready for the data science step, but rather need to set up their basic transformed tables from the raw data and multiple inputs. They give that an entire job title now also: data engineer. Urghh. I’d prefer someone who can stand up the tables.. and do some basic analytics for the company’s needs. Data science is more relevant to the larger companies who have data sets sorted out.

@mahdirahman8451 Год назад

Why is there so many calculus books, are they any different?

@pushkarnagpure2357 Год назад

Thanks

@khaledfarrag9754 Год назад

could you make video about learning statistics for data science and linear algebra

@pato750 Год назад

what about Calculus of Vector Functions , Williamson, Crowell, and Trotter? for vector calculus? I loved that book, I use as undergrad in argentina. The linear algebra (friedberg) cover it's interesting...I wonder why they choose that cover

@amydebuitleir Год назад

I've used both Python and R fairly extensively, and I recommend learning Python. Python is a general-purpose language, so you can use it for a lot of tasks, not just data mining. R, on the other hand, is a special-purpose language that you are unlikely to want to use for anything else. The programming skills you use for Python are readily transferable to other languages, while for R you'll have to learn some structures and syntax that are unlike other languages. Both languages have packages that provide essentially the same capability.

@roch5547 Год назад

Hi/Hola. Could you do a review of the book "The Elements of statistical Learning. Data mining, inferences and predictions " or introduction to statical learning. Please

@afzaal919 Год назад

Please make a list of these books

@TheMathSorcerer Год назад

it's in the description:)

@afzaal919 Год назад

@@TheMathSorcerer thanks

@userwheretogo Год назад

pretty much all these can be thrown away for typical DS job

@truongdangmanh7471 Год назад

Even though I think I know some of the linear algebra for machine learning, I never managed to truly read through all of a single book about it. I just tried then stopped after some time then by that time I forgot everything, I just don't know how to keep going.