🔖 The Legendary MIT Textbook on Mathematics for Computer Science

Mathematics for Computer Science is one of the best free textbooks for developers, ML engineers, and data scientists.

It contains over 1000 pages covering discrete mathematics, logic, graphs, probability, combinatorics, recurrence relations, and other fundamental topics.

⛓️ Link to the textbook:
https://people.csail.mit.edu/meyer/mcs.pdf

#ComputerScience #Mathematics #MachineLearning #DataScience #MIT #OpenSource

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Combining Plots in Matplotlib 📊

In Matplotlib, you can easily combine multiple plots in a single window using the subplot() function. Simply create the necessary plots, specify their layout, add titles, and you'll get a clear visualization for easy data comparison.

#Matplotlib #DataVisualization #Python #DataScience #Coding #Plotting

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Reinforcement Learning Methods and Tutorials 🧠📚

In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.

Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow 🚀

Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms. 📖✨

#ReinforcementLearning #MachineLearning #AI #DeepLearning #TechTutorials #DataScience

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Feature Scaling: Why Feature Scaling Affects Model Training

Feature scaling is often overlooked because it seems like just another data preprocessing step. However, in practice, it often helps models train faster and more stably. Imagine one feature has values ranging from 0 to 1, while another has values ranging from 0 to 10,000. Although both features may be equally important for prediction, it's more difficult for the optimizer to work with such data.

This means it has to take more steps to find a good solution. Additionally, regularization becomes less effective because features with different scales require coefficients of different magnitudes. Let's look at how this looks in a simple example.

Install dependencies:

pip install numpy scikit-learn

Import libraries:

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score

Let's create a small synthetic dataset. It will have two features: the first has a normal scale, and the second is about a thousand times larger.

Importantly, both features actually influence the target variable. That is, the only difference between them is the scale.

np.random.seed(42)
x_small = np.random.normal(0, 1, 300)
x_large = np.random.normal(0, 1000, 300)

X = np.vstack([x_small, x_large]).T

y = (x_small + 0.001 * x_large > 0).astype(int)

Now, let's split the data into training and testing sets. We won't scale anything yet—first, let's see how the model behaves on the original data.

X_train, X_test, y_train, y_test = train_test_split(
    X, y,
    test_size=0.3,
    random_state=42,
    stratify=y
)

Let's train a logistic regression model without scaling.

In addition to the model's quality, let's also look at the number of iterations (n_iter_). This metric shows how much work the optimizer had to do to find the coefficients.

model = LogisticRegression()
model.fit(X_train, y_train)

pred = model.predict_proba(X_test)[:, 1]

print("ROC-AUC:", roc_auc_score(y_test, pred))
print("Iterations:", model.n_iter_)

Now, let's scale the features to the same scale using StandardScaler.

It calculates the mean and standard deviation only for the training set and then uses the same values for the test set. This is important because the model should not "peek" at the test data during training.

After this transformation, both features are approximately on the same scale, and it becomes easier for the optimizer to work with them.

scaler = StandardScaler()

X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

Now, let's retrain the model.

We're using the same model, the same data, and the same parameters. The only difference is that the features are now scaled.

model = LogisticRegression()
model.fit(X_train_scaled, y_train)

pred = model.predict_proba(X_test_scaled)[:, 1]

print("ROC-AUC (scaled):", roc_auc_score(y_test, pred))
print("Iterations (scaled):", model.n_iter_)

Most often, the ROC-AUC doesn't change much. However, the number of iterations becomes smaller. This means that the optimizer found a solution faster, and the training was more stable.

🔥 Feature scaling is a simple data preprocessing step that, in many cases, allows the model to train faster and more stably. For logistic regression, SVMs, neural networks, and other algorithms that use numerical optimization, it's best not to skip it.

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Foundations of Applied Mathematics is a free series of four textbooks created for the applied and computational mathematics program at Brigham Young University. 📚

The series includes four volumes:
*   Mathematical Analysis
*   Algorithms, Approximation, and Optimization
*   Uncertainty and Data
*   Dynamics and Control

The series is suitable for upper-level undergraduate and introductory graduate students. It also includes Python lab exercises and practical assignments, connecting mathematical theory with numerical computation, algorithms, data analysis, and scientific applications. 🐍

I particularly appreciate that these are not just theoretical textbooks. The accompanying Python materials help to illustrate how these concepts are applied to real-world computational problems. 💻

https://foundations-of-applied-mathematics.github.io

#Mathematics #Python #Education #DataScience #Algorithms #Learning

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A Powerful Alternative to Pandas 🚀

This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. ⚙️

To get started, simply replace a single import:

import fireducks.pandas as pd

Performance Benchmarks demonstrate speed improvements in various use cases. 📈

More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing

#Pandas #Python #DataScience #Performance #Fireducks #BigData

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🔖 Learning Data Science through interactive examples

One of the most useful repositories for those who want to better understand machine learning.

It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results.

⛓ Link to GitHub
https://github.com/GeostatsGuy/DataScienceInteractivePython

#DataScience #MachineLearning #Python #Learning #Tech #GitHub

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🔖 Over 300 real-world case studies of ML systems from top companies. 🤖

We found a repository that collects genuine ML engineering experience – not theory from textbooks, but real stories of implementing models in production. 📚

Inside, you'll find case studies from Uber, Netflix, Google, and other companies: how they built the architecture, what problems arose, where the systems failed, and what solutions helped them recover. 🏗️

⛓ Link to GitHub
https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies

#MachineLearning #MLCaseStudies #DataScience #Engineering #Uber #Netflix

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🚀 TOP 8 Machine Learning Regression Metrics Explained

Choosing the right metric isn't academic; it's the difference between a model that works in production and one that breaks trust.

Here's the map every ML engineer should carry in 2026:

1️⃣ MEAN ABSOLUTE ERROR (MAE)
Average miss, easy to explain. On average, we're off by 5 units.

2️⃣ MEAN SQUARED ERROR (MSE)
Squares mistakes → big errors hurt more.

3️⃣ ROOT MEAN SQUARED ERROR (RMSE)
Square root of MSE. Same unit as the target, easier to relate.

4️⃣ R² COEFFICIENT
Explains how much variation your model captures. But don't confuse fit with usefulness.

5️⃣ ADJUSTED R²
Keeps R² honest. Extra useless features won't inflate the score.

6️⃣ MAPE (Mean Absolute Percentage Error)
Errors in percentages. Great for business dashboards, weak if actual values get near zero.

7️⃣ Huber Loss
Blends MAE & MSE. Punishes small errors like MSE, resists outliers like MAE.

8️⃣ Quantile Loss
Perfect when predicting ranges instead of single points like demand at the 90th percentile.

👁 VIEW

● = Actuals ○ = Predictions

MAE → avg |●-○|
MSE → avg (●-○)²
RMSE → √MSE
R² → variance explained
MAPE → % error
Huber → balance (MSE + MAE)
Quant → percentile accuracy

🏆 THE TAKEAWAY
Metrics decide what success looks like.
Choose wrong, and your good model is useless.
Choose right, and you build trust, adoption, and impact.

📝 TL;DR
MAE → simple error
MSE → punishes big errors
RMSE → interpretable scale
R² → fit, not prediction power
Adj R² → guards against overfitting
MAPE → % view, fragile near zero
Huber → outlier-resistant
Quantile → forecasts ranges

#MachineLearning #DataScience #RegressionMetrics #MLOps #AI #TechTips

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Python And Flask Framework Complete Course

Python-Powered Proficiency: Depth Introduction To Python Programming And Python Web Framework Flask.…

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"Introduction to Machine Learning" is another free textbook on machine learning, approximately 600 pages long, which emphasizes a deep mathematical understanding of the subject. 📚🧮

The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: linear and logistic regression, the k-nearest neighbors method, decision trees, random forests, boosting, and neural networks. 🤖📈

A significant portion of the book is dedicated to probabilistic and generative models. It discusses Monte Carlo methods, graphical models, Bayesian networks, variational methods, normalizing flows, variational autoencoders (VAEs), and generative adversarial networks (GANs). 🎲🧠

The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊

In my opinion, this is an excellent resource for those who want to gain a broad understanding of machine learning and understand the mathematics underlying the key methods, rather than treating them as "black boxes." 💡✨

https://arxiv.org/pdf/2409.02668

#MachineLearning #DeepLearning #AI #Mathematics #DataScience #NeuralNetworks

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