Data Science Machine Learning Data Analysis

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Machine Learning Glossary

Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.

Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.html

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Data Science Machine Learning Data Analysis

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MIT has updated its famous course 6.S191: Introduction to Deep Learning.

.

All slides, #code and additional materials can be found at the link provided.

📌 Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew

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Data Science Machine Learning Data Analysis

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Numpy @CodeProgrammer.pdf

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🏳️‍🌈 "NumPy Library" Tutorial

👨🏻‍💻 For the past few days, I've been busy preparing this comprehensive tutorial on the NumPy library for data science, trying to cover all the tips and tricks of this library.

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Why is this booklet different? Because it is not written based on just theoretical concepts, but is the result of my own experiences and learning. It has real and practical examples that will help you better understand #NumPy concepts and use them in your projects.💯

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Data Science Machine Learning Data Analysis

👩‍💻 Prompt Engineering: A Practical Example

This real-world project tutorial covers zero-shot and few-shot prompting, delimiters, numbered steps, role prompts, chain-of-thought prompting, and more. Improve your LLM-assisted projects today.

Link: https://realpython.com/practical-prompt-engineering/

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Data Science Machine Learning Data Analysis

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🔖 The book that paved the way for me to "data science"!

👨🏻‍💻 "Where do I start now?" This was the first and biggest question I faced when I started my Data Science learning journey!

⏪ I was really overwhelmed by the large number of scattered sources, long courses, and specialized books full of heavy terminology. I didn't know how to start and move forward in this direction...

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✏️ This book is a complete guide to starting from scratch and is great for both beginners and professionals in this field!! From coding with Python to working with data, visualization, and even AI tools, it explains everything in the simplest and most practical way possible.

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┌ 🏳️‍🌈 Intro to Data Science with Python
├ 📄 E-book
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Data Science Machine Learning Data Analysis

Forwarded from Python | Machine Learning | Coding | R

course lecture on building Transformers from first principles:
https://www.dropbox.com/scl/fi/jhfgy8dnnvy5qq385tnms/lectureattentionneuralnetworks.pdf?rlkey=fddnkonsez76mf8bzider3hrv&dl=0

The #PyTorch notebooks also demonstrate how to implement #Transformers from scratch:
https://github.com/xbresson/CS52422025/tree/main/labslecture07

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Data Science Machine Learning Data Analysis

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Pandas Introduction to Advanced.pdf

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📄

"Pandas Introduction to Advanced" booklet

👨🏻‍💻 You can't attend a #datascience interview and not be asked about Pandas! But you don't have to memorize all its methods and functions! With this booklet, you'll learn everything you need.

✔️ One of the most useful and interesting combinations is using #Pandas with #AWS Lambda, which can be very useful in real projects.

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Data Science Machine Learning Data Analysis

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Data Science Machine Learning Data Analysis

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Exercises in Machine Learning

This book contains 75+ exercises

Download, read, and practice:
arxiv.org/pdf/2206.13446

GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises

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Data Science Machine Learning Data Analysis

Forwarded from Python | Machine Learning | Coding | R

Linear Algebra

The 2nd best book on linear algebra with ~1000 practice problems. A MUST for AI & Machine Learning.

Completely FREE.

Download it: https://www.cs.ox.ac.uk/files/12921/book.pdf

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Data Science Machine Learning Data Analysis

#MachineLearning Systems — Principles and Practices of Engineering Artificially Intelligent Systems: https://mlsysbook.ai/

open-source textbook focuses on how to design and implement AI systems effectively

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Data Science Machine Learning Data Analysis

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🔗 Machine Learning from Scratch by Danny Friedman

https://dafriedman97.github.io/mlbook/content/introduction.html

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Data Science Machine Learning Data Analysis

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"Introduction to Probability for Data Science"

One of the best books on #Probability. Available FREE.

Download the book:
probability4datascience.com/download.html

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Data Science Machine Learning Data Analysis

Forwarded from Data Science | Machine Learning with Python for Researchers

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◀️ Coding popular ML algorithms from scratch

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◀️ Fully prepared for job interviews

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◀️ Learning how to build and deploy models.

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◀️ Basic mathematical concepts necessary to understand machine learning.

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◀️ Learn algorithms with practical examples.

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Machine learning with a probabilistic approach

◀️ Better understanding modeling and uncertainty with a statistical perspective.

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◀️ Deep understanding of machine learning concepts with conceptual teaching from one of the leading professors in the field of ML,

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◀️ A strong start in the world of neural networks, CNNs and RNNs.

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◀️ Intuitive and visual teaching of linear algebra concepts.

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Explainable models in machine learning

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Data Science Machine Learning Data Analysis

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from SQL to pandas.pdf

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🐼

"Comparison Between SQL and pandas" – A Handy Reference Guide

⚡️ As a data scientist, I often found myself switching back and forth between SQL and pandas during technical interviews. I was confident answering questions in SQL but sometimes struggled to translate the same logic into pandas – and vice versa.

🔸 To bridge this gap, I created a concise booklet in the form of a comparison table. It maps SQL queries directly to their equivalent pandas implementations, making it easy to understand and switch between both tools.

⚡ This reference guide has become an essential part of my interview prep. Before any interview, I quickly review it to ensure I’m ready to tackle data manipulation tasks using either SQL or pandas, depending on what’s required.

📕 Whether you're preparing for interviews or just want to solidify your understanding of both tools, this comparison guide is a great way to stay sharp and efficient.

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Data Science Machine Learning Data Analysis

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Numpy from basics to advanced.pdf

2.4 MB

📕

Mastering NumPy – From Basics to Advanced

NumPy is an essential library in the world of data science, widely recognized for its efficiency in numerical computations and data manipulation. This powerful tool simplifies complex operations with arrays, offering a faster and cleaner alternative to traditional Python lists and loops.

The "Mastering NumPy" booklet provides a comprehensive walkthrough—from array creation and indexing to mathematical/statistical operations and advanced topics like reshaping and stacking. All concepts are illustrated with clear, beginner-friendly examples, making it ideal for anyone aiming to boost their data handling skills.

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Join the communities:

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Data Science Machine Learning Data Analysis

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Polars.pdf

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📖 A comprehensive cheat sheet for working with Polars

🌟 Have you ever worked with pandas and thought that was the fastest way? I thought the same thing until I worked with Polars.

✏️ This cheat sheet explains everything about Polars in a concise and simple way. Not just theory! But also a bunch of real examples, practical experience, and projects that will really help you in the real world.

┌ 🐻‍❄️ Polars Cheat Sheet
├ ♾️ Google Colab
└ 📖 Doc

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Data Science Machine Learning Data Analysis

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🥇 40+ Real and Free Data Science Projects

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Data Science Machine Learning Data Analysis

Topic: Python SciPy – From Easy to Top: Part 5 of 6: Working with SciPy Statistics

---

1. Introduction to `scipy.stats`

• The scipy.stats module contains a large number of probability distributions and statistical functions.
• You can perform tasks like descriptive statistics, hypothesis testing, sampling, and fitting distributions.

---

2. Descriptive Statistics

Use these functions to summarize and describe data characteristics:

from scipy import stats
import numpy as np

data = [2, 4, 4, 4, 5, 5, 7, 9]

mean = np.mean(data)
median = np.median(data)
mode = stats.mode(data, keepdims=True)
std_dev = np.std(data)

print("Mean:", mean)
print("Median:", median)
print("Mode:", mode.mode[0])
print("Standard Deviation:", std_dev)

---

3. Probability Distributions

SciPy has built-in continuous and discrete distributions such as normal, binomial, Poisson, etc.

Normal Distribution Example

from scipy.stats import norm

# PDF at x = 0
print("PDF at 0:", norm.pdf(0, loc=0, scale=1))

# CDF at x = 1
print("CDF at 1:", norm.cdf(1, loc=0, scale=1))

# Generate 5 random numbers
samples = norm.rvs(loc=0, scale=1, size=5)
print("Random Samples:", samples)

---

4. Hypothesis Testing

One-sample t-test – test if the mean of a sample is equal to a known value:

sample = [5.1, 5.3, 5.5, 5.7, 5.9]
t_stat, p_val = stats.ttest_1samp(sample, popmean=5.0)

print("T-statistic:", t_stat)
print("P-value:", p_val)

Interpretation: If the p-value is less than 0.05, reject the null hypothesis.

---

5. Two-sample t-test

Test if two samples come from populations with equal means:

group1 = [20, 22, 19, 24, 25]
group2 = [28, 27, 26, 30, 31]

t_stat, p_val = stats.ttest_ind(group1, group2)

print("T-statistic:", t_stat)
print("P-value:", p_val)

---

6. Chi-Square Test for Independence

Use to test independence between two categorical variables:

# Example contingency table
data = [[10, 20], [20, 40]]
chi2, p, dof, expected = stats.chi2_contingency(data)

print("Chi-square statistic:", chi2)
print("P-value:", p)

---

7. Correlation and Covariance

Measure linear relationship between variables:

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

corr, _ = stats.pearsonr(x, y)
print("Pearson Correlation Coefficient:", corr)

Covariance:

cov_matrix = np.cov(x, y)
print("Covariance Matrix:\n", cov_matrix)

---

8. Fitting Distributions to Data

You can fit a distribution to real-world data:

data = np.random.normal(loc=50, scale=10, size=1000)
params = norm.fit(data)  # returns mean and std dev

print("Fitted mean:", params[0])
print("Fitted std dev:", params[1])

---

9. Sampling from Distributions

Generate random numbers from different distributions:

# Binomial distribution
samples = stats.binom.rvs(n=10, p=0.5, size=10)
print("Binomial Samples:", samples)

# Poisson distribution
samples = stats.poisson.rvs(mu=3, size=10)
print("Poisson Samples:", samples)

---

10. Summary

• scipy.stats is a powerful tool for statistical analysis.
• You can compute summaries, perform tests, model distributions, and generate random samples.

---

Exercise

• Generate 1000 samples from a normal distribution and compute mean, median, std, and mode.
• Test if a sample has a mean significantly different from 5.
• Fit a normal distribution to your own dataset and plot the histogram with the fitted PDF curve.

---

#Python #SciPy #Statistics #HypothesisTesting #DataAnalysis

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Data Science Machine Learning Data Analysis

Python Commands for Data Cleaning

#Python #DataCleaning #DataAnalytics #DataScientists #MachineLearning #ArtificialIntelligence #DataAnalysis

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