✅ Free Book Online

Artificial Intelligence in Business Gets Real

By S. Ransbotham, P. Gerbert, M. Reeves, D. Kiron, and M. Spira

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✅ Problem Solving with Algorithms and Data Structures using Python

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An archive of all O'Reilly data ebooks is available below for free download. Dive deep into the latest in data science and big data, compiled by O'Reilly editors, authors, and Strata speakers:

There are several selections starting from 2012 Ebooks to 2016 Ebooks.
#Book #کتاب
To download O'Reilly data ebooks,🌎 click here.

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A Programmer’s Introduction to Mathematics. It teaches someone with programming knowledge and experience how to engage with mathematics. Achieve this goal largely because of the implicit overlap in the content and ways of thinking between math and programming.
Until now. If you’re a programmer who wants to really grok math, this book is for you.
GitHub: Link
#Book #کتاب

Download :
https://t.me/ai_python_en/190

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Not everyone knows but my #book has its Github repository where all #Python code used to build illustrations is gathered.

So, while reading the book, you can actually run the described #algorithms, play with hyperparameters and #datasets, and generate your versions of illustrations.

https://github.com/aburkov/theMLbook

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As the author states: "work in process and even in an early dirty phase"
But still very cool 🙂

Book: Predictive Models: Visual Exploration, Explanation and Debugging With examples in R and Python By Przemyslaw Biecek


#book #datascience #machinelearning #statistics #programming_language

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There's increasing interest in time-series analysis in the #DataScience community. I see this as a very positive development, as TSA has seldom gotten the attention from data scientists I feel it merits.

Econometricians and statisticians in other fields such as meteorology have been analyzing time-series data for many decades. Before diving into LSTMs and other machine learning tools - which statisticians also use - I think it would be wise to read up on other methods statisticians employ and what they've learned from more than a century of experience.

Here are some good books on TSA:

- Time Series Analysis (Wei)
- Time Series Analysis and Its Applications (Shumway and Stoffer)
- Forecasting (Hyndman and Athanasopoulos)
- Multiple Time-Series Analysis (Lütkepohl)
- Time Series Analysis by State Space Methods (Durbin and Koopman)
- Time Series Modelling with Unobserved Components (Pelagatti)
- Hidden Markov Models for Time Series (Zucchini)
- GARCH Models (Francq and Zakoïan)
- Nonparametric Econometrics (Li and Racine)

There are academic journals as well. (I subscribed to the Journal of Forecasting and the Journal of Time Series Analysis until they jacked up their subscription fees.)

TSA is a difficult topic, IMO, but more important than ever in the age of big data.
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There are now many methods we can use when our dependent variable is not continuous. SVM, XGBoost and Random Forests are some popular ones.

There are also "traditional" methods, such as Logistic Regression. These usually scale well and, when used properly, are competitive in terms of predictive accuracy.

They are probabilistic models, which gives them additional flexibility. They also are often easier to interpret, critical when the goal is explanation, not just prediction.

They can be more work, however, and are probably easier to misuse than newer methods such as Random Forests. Here are some excellent books on these methods that may be of interest:

- Categorical Data Analysis (Agresti)
- Analyzing Categorical Data (Simonoff)
- Regression Models for Categorical Dependent Variables (Long and Freese)
- Generalized Linear Models and Extensions (Hardin and Hilbe)
- Regression Modeling Strategies (Harrell)
- Applied Logistic Regression (Hosmer and Lemeshow)
- Logistic Regression Models (Hilbe)
- Analysis of Ordinal Categorical Data (Agresti)
- Applied Ordinal Logistic Regression (Liu)
- Modeling Count Data (Hilbe)
- Negative Binomial Regression (Hilbe)
- Handbook of Survival Analysis (Klein et al.)
- Survival Analysis: A Self-Learning Text (Kleinbaum and Klein)

#statistics #book #Machinelearning

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I do currently use Python but, since it's quite popular among data scientists, I've looked into it and have read several #book s on #Python:

- A Primer on Scientific Programming with Python (Langtangen)
- Python for Data Analysis (McKinney)
- Python Data Science Essentials (Boschetti and Massaron)
- Machine Learning in Python (Bowles)
- Hands-On Predictive Analytics with Python (Fuentes)
- Data Science for Marketing Analytics (Blanchard et al.)
- Bayesian Analysis with Python (Martin)
- Web Scraping with Python (Lawson)

I have found all of them helpful in different ways, including offering different perspectives on data science. Several are well-known, but I cannot critique them as an experienced Python user, so this is just FYI.

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In my line of work, I often need to model multiple dependent (outcome) variables simultaneously.

These may be latent variables, each with several indicator (observed) variables, observed variables, or a combination of the two.

Structural Equation Modeling (SEM) is one method able to handle these situations. Observed variables do not have to be continuous and, in fact, can be a mix of data types, including count variables and binary indicators.

Here are some excellent #book s on SEM for those who'd like to learn more about this method:

- Principles and Practice of Structural Equation Modeling (Kline)
- Linear Causal Modeling with Structural Equations (Mulaik)
- Structural Equations with Latent Variables (Bollen)
- Handbook of Structural Equation Modeling (Hoyle)

It's not an easy method for most of us to master, however, and new developments are happening at a rapid pace. Structural Equation Modeling: A Multidisciplinary Journal (Routledge) is an excellent resource for experienced SEM modelers.

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5 Computer Vision Textbooks

Textbooks are those books written by experts, often academics, and are designed to be used as a reference for students and practitioners.

They focus mainly on general methods and theory (math), not on the practical concerns of problems and the application of methods (code).

The top five textbooks on computer vision are as follows (in no particular order):

🔸 Computer Vision: Algorithms and Applications, 2010.
🔸 Computer Vision: Models, Learning, and Inference, 2012.
🔸 Computer Vision: A Modern Approach, 2002.
🔸 Introductory Techniques for 3-D Computer Vision, 1998.
🔸 Multiple View Geometry in Computer Vision, 2004.

Top 3 Computer Vision Programmer Books

Programmer #book s are playbooks (e.g. O’Reilly books) written by experts, often developers and engineers, and are designed to be used as a reference by practitioners.


🔸 Learning OpenCV 3, 2017.
🔸 Programming Computer Vision with Python, 2012.
🔸 Practical Computer Vision with SimpleCV, 2012.

#ComputerVision

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A free linear algebra #textbook with solutions by Jim Hefferon. This knowledge will be very useful for understanding #machinelearning and beyond.

http://joshua.smcvt.edu/linearalgebra/#current_version

#book

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