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Cybersecurity Cheatsheet
#Cybersecurity
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A great picture to understand Linux File Permissions.
#Linux
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〓 @SecBookSs
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Artificial Intelligence, Game Theory and Mechanism Design in Politics
Tshilidzi Marwala, 2023
#Artificial_Intelligence #AI
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Data Structure Cheat Sheet
#Data #Data_Structure
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Git Commands Cheat Sheet
#Git #Commands
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〓 @SecBookSs
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📺 Top 12 YouTube Channels to Learn Python
👩💻 Python will include 57% of data scientist job ads in 2024 . It is still the number one programming language for data scientists.
✅ Now, if you are looking for the best resources to improve your Python skills, after searching and reviewing various resources, I have prepared a list of 12 top channels that provide first-class Python training, which can turn beginners into professional Python programmers.
🎬 Python Programmer channel
┤ 📈 211 videos / 465K SUB
┘ 🔴 Link: gilesmcmullen">Python Programmer
🎬 Luke Barousse channel
┤ 📈 157 videos / 429K SUB
┘ 🔴 Link: LukeBarousse">Luke Barousse
🎬 codebasics channel
┤ 📈 837 videos / 990K SUB
┘ 🔴 link: codebasics">codebasics
🎬 StatQuest channel with Josh Starmer
┤ 📈 271 videos / 1.14M SUB
┘ 🔴 Link: statquest">StatQuest with Josh Starmer
🎬 Sundas Khalid channel
┤ 📈 143 videos / 203K SUB
┘ 🔴 Link: SundasKhalid">Sundas Khalid
🎬 Shashank Kalanithi channel
┤ 📈 152 videos / 148K SUB
┘ 🔴 Link: ShashankData">Shashank Kalanithi
🎬 Programming with Mosh channel
┤ 📈 203 videos / 3.85M SUB
┘ 🔴 Link: programmingwithmosh">Programming with Mosh
🎬 Corey Schafer channel
┤ 📈 233 videos / 129K SUB
┘ 🔴 Link: coreyms">Corey Schafer
🎬 sentdex channel
┤ 📈 1254 videos / 1.3M SUB
┘ 🔴 link: sentdex">sentdex
🎬 Patrick Loeber channel
┤ 📈 206 videos / 264K SUB
┘ 🔴 Link: patloeber">Patrick Loeber
🎬 Socratica channel
┤ 📈 659 videos / 876K SUB
┘ 🔴 Link: Socratica">Socratica
🎬 Tech With Tim channel
┤ 📈 983 videos / 1.48M SUB
┘ 🔴 Link: TechWithTim">Tech With Tim
#Python #Programming
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Python for Students
#ZIP #Python #Programming
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Raspberry Pi OS System Administration with systemd and Python
Robert M. Koretsky, 2023
#PDF #Python #Programming #RaspberryPi
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Python Tips and Tricks
Benjamin Bennett Alexander, 2022
#PDF #Python #Programming
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Ethical hacking tools for recon
#Ethical_Hacking
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CHATGPT Cheat Sheet.
#CHATGPT #AI
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〓 @SecBookSs
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🖥 10 Advanced Python Scripts For Everyday Programming
🔸1. SpeedTest with Python
# pip install pyspeedtest
# pip install speedtest
# pip install speedtest-cli
#method 1
import speedtest
speedTest = speedtest.Speedtest()
print(speedTest.get_best_server())
#Check download speed
print(speedTest.download())
#Check upload speed
print(speedTest.upload())
# Method 2
import pyspeedtest
st = pyspeedtest.SpeedTest()
st.ping()
st.download()
st.upload()
🔹2. Search on Google# pip install google
from googlesearch import search
query = "Medium.com"
for url in search(query):
print(url)
🔸3. Make Web Bot# pip install selenium
import time
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
bot = webdriver.Chrome("chromedriver.exe")
bot.get('[http://www.google.com'](http://www.google.com'))
search = bot.find_element_by_name('q')
search.send_keys("@codedev101")
search.send_keys(Keys.RETURN)
time.sleep(5)
bot.quit()
🔹4. Fetch Song Lyrics# pip install lyricsgenius
import lyricsgenius
api_key = "xxxxxxxxxxxxxxxxxxxxx"
genius = lyricsgenius.Genius(api_key)
artist = genius.search_artist("Pop Smoke", max_songs=5,sort="title")
song = artist.song("100k On a Coupe")
print(song.lyrics)
🔸5. Get Exif Data of Photos# Get Exif of Photo
# Method 1
# pip install pillow
import PIL.Image
import PIL.ExifTags
img = PIL.Image.open("Img.jpg")
exif_data =
{
PIL.ExifTags.TAGS[i]: j
for i, j in img._getexif().items()
if i in PIL.ExifTags.TAGS
}
print(exif_data)
# Method 2
# pip install ExifRead
import exifread
filename = open(path_name, 'rb')
tags = exifread.process_file(filename)
print(tags)
🔹6. OCR Text from Image# pip install pytesseract
import pytesseract
from PIL import Image
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
t=Image.open("img.png")
text = pytesseract.image_to_string(t, config='')
print(text)
🔸7. Convert Photo into Cartonize# pip install opencv-python
import cv2
img = cv2.imread('img.jpg')
grayimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
grayimg = cv2.medianBlur(grayimg, 5)
edges = cv2.Laplacian(grayimg , cv2.CV_8U, ksize=5)
r,mask =cv2.threshold(edges,100,255,cv2.THRESH_BINARY_INV)
img2 = cv2.bitwise_and(img, img, mask=mask)
img2 = cv2.medianBlur(img2, 5)
cv2.imwrite("cartooned.jpg", mask)
🔹8. Empty Recycle Bin# pip install winshell
import winshell
try:
winshell.recycle_bin().empty(confirm=False, /show_progress=False, sound=True)
print("Recycle bin is emptied Now")
except:
print("Recycle bin already empty")
🔸9. Python Image Enhancement# pip install pillow
from PIL import Image,ImageFilter
from PIL import ImageEnhance
im = Image.open('img.jpg')
# Choose your filter
# add Hastag at start if you don't want to any filter below
en = ImageEnhance.Color(im)
en = ImageEnhance.Contrast(im)
en = ImageEnhance.Brightness(im)
en = ImageEnhance.Sharpness(im)
# result
en.enhance(1.5).show("enhanced")
🔹10. Get Window Version# Window Version
import wmi
data = wmi.WMI()
for os_name in data.Win32_OperatingSystem():
print(os_name.Caption) # Microsoft Windows 11 Home
#Python #Programming #Scripts
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Why is python best for data science?
#Python #Data_Science
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Cybersecurity All-in-One For Dummies (2022)
#PDF #Cybersecurity
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〓 @SecBookSs
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O'Reilly Media, one of the most reputable publishers in the fields of programming, data mining, and AI, has made 10 data science books available to those interested in this field for free .
To use the online and PDF versions of these books, you can use the following links:
1. Python Data Science Handbook
┌ Online
└ PDF
2. Python for Data Analysis book
┌ Online
└ PDF
3. Fundamentals of Data Visualization book
┌ Online
└ PDF
4. R for Data Science book
┌ Online
└ PDF
5. Deep Learning for Coders book
┌ Online
└ PDF
6. DS at the Command Line book
┌ Online
└ PDF
7. Hands-On Data Visualization Book
┌ Online
└ PDF
8. Think Stats book
┌ Online
└ PDF
9. Think Bayes book
┌ Online
└ PDF
10. Kafka, The Definitive Guide
┌ Online
└ PDF
#DataScience #Python #DataAnalysis #DataVisualization #RProgramming #DeepLearning #CommandLine #HandsOnLearning #Statistics #Bayesian #Kafka #MachineLearning #AI #Programming #FreeBooks
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📱 Python CheatSheet
1. Basic Syntax
- Print Statement: print("Hello, World!")
- Comments: # This is a comment
2. Data Types
- Integer: x = 10
- Float: y = 10.5
- String: name = "Alice"
- List: fruits = ["apple", "banana", "cherry"]
- Tuple: coordinates = (10, 20)
- Dictionary: person = {"name": "Alice", "age": 25}
3. Control Structures
- If Statement:
if x > 10:
print("x is greater than 10")
for fruit in fruits:
print(fruit)
while x < 5:
x += 1
def greet(name):
return f"Hello, {name}!"
add = lambda a, b: a + btry:
result = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero.")
with open('file.txt', 'r') as file:
content = file.read()
with open('file.txt', 'w') as file:
file.write("Hello, World!")
squared = [x**2 for x in range(10)]even_squares = [x**2 for x in range(10) if x % 2 == 0]import mathfrom math import sqrtimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltclass Dog:
def __init__(self, name):
self.name = name
def bark(self):
return "Woof!"
python -m venv myenvmyenv\Scripts\activatesource myenv/bin/activatepython script.pypip install package_namepip list
Cybersecurity Roadmap 2024
#Cybersecurity
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〓 @SecBookSs
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6 Free Cloud Storage Services
1. Google Drive
- URL: https://drive.google.com
- Description: You know this one
2. Dropbox
- URL: https://dropbox.com
- Description: Dropbox provides a user-friendly platform for file storage and sharing, with collaborative features for efficient team collaboration.
3. OneDrive
- URL: https://onedrive.live.com
- Description: OneDrive by Microsoft offers cloud storage with seamless integration into Windows and Office applications, ensuring easy file access and sharing.
4. Box
- URL: https://box.com
- Description: It is a cloud content management platform that facilitates secure file storage, sharing, and collaboration for businesses and individuals.
5. Mast
-URL: mastpanel.online
- Description: it is an online Services Platform which you can get cheapest social media services.
6. MEGA
- URL: https://mega.nz
- Description: It provides end-to-end encrypted cloud storage, ensuring the privacy and security of your files
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💻 Useful Windows 11 features. New shortcuts.
• Win+A - will open the quick sound setup menu, Wi-Fi and mini player.
• Win+N - also in Windows 10 opens notifications, but now it includes the calendar.
• Win+Z - opens a small multi-window setup window.
• Win+H - this is a voice-recognition voice recorder that already knows how to work with the Russian language. For its work it is enough to open any input field, and then press the above buttons and start talking.
#Windows #shortcuts
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🔆 Free Data Science Useful Resources
• Intro to Data Science
🔗 https://imp.i115008.net/rn2beD
• Data Analysis and Visualization
🔗 https://imp.i115008.net/JrBjZR
• Data Analysis with R by Facebook
🔗 https://imp.i115008.net/gbJr5r
• Data Science Interview Questions and Answers
🔗 /channel/datasciencefun/958
• Data Analysis with Python and PySpark
🔗 /channel/learndataanalysis/44
• Introduction to Data Science using Python
🔗 https://ern.li/OP/1qvkxbfaxqj
• Intro to Data for Data Science
🔗 https://ern.li/OP/1qvkxbfbmf8
• Data Science with Analogies, Algorithms and Solved Problems
🔗 https://ern.li/OP/1qvkxbfcehz
• Introduction to Data Science for Complete Beginners
🔗 https://bit.ly/3sh4oPO
• Data Science Cheat sheet
🔗 https://github.com/aaronwangy/Data-Science-Cheatsheet
#Data_Science #Python
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🔖 Python for Students
Author: Julie Nisbet
Date: 2019-11-01
Duration: 1h 8mLearn programming with Python in this fun and easy-to-follow course designed for high-school and college-age students.
#Python #Programming
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☑️ 10 AI tools that can save you hours:
1. http://Durable.co → AI website builder
2. http://Consensus.app → AI academic research
3. http://Rytr.me → Personal AI copywriter
4. http://Bardeen.ai → Automating tasks with AI
5. http://Taskade.com → Brain powered by AI
6. http://Quillbot.com → Reformulate your texts
7. http://Leonardo.ai → Generate realistic photos
8. http://Otter.ai → AI takes notes for you
9. http://Boomy.com → Create music in seconds
10. http://Clipdrop.co → Personal AI copywriter
Try them, they’re awesome!
#AI
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〓 @SecBookSs
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50 Cybersecurity Project Ideas For Beginners To Expert
#Cybersecurity
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〓 @SecBookSs
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18 Most used Linux commands
#Linux
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〓 @SecBookSs
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Python Cheat Sheet
#Python #Programming
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5 Sites to Level Up Your Coding Skills 👨💻👩💻
🔹 leetcode.com
🔹 hackerrank.com
🔹 w3schools.com
🔹 datasimplifier.com
🔹 hackerearth.com
#Programming
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〓 @SecBookSs
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🔟 Python resources to boost your resume in 2023
𝟭. 𝗜𝗻𝘁𝗿𝗼 𝘁𝗼 𝗣𝘆𝘁𝗵𝗼𝗻
This a great course to get started with learning Python, if you have no coding experience.
👉 https://kaggle.com/learn/intro-to-programming
𝟮. 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗰𝗼𝘂𝗿𝘀𝗲
Learn the fundamentals like functions, loops, conditional statements, etc of the most important language for data science.
👉 https://kaggle.com/learn/python
𝟯. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻
Part 1 prepares you for PCEP – Certified Entry-Level Python Programmer Certification.
Part 2 prepares you for PCAP – Certified Associate in Python Programming Certification.
👉 https://netacad.com/courses/programming/pcap-programming-essentials-python
𝟰. Python Data Structure and Algorithms
👉 /channel/programming_guide/76
𝟱. 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
You'll learn Python fundamentals like variables, loops, conditionals, and functions.
Then you'll quickly ramp up to complex data structures, networking, relational databases, and data visualization.
👉 https://freecodecamp.org/learn/scientific-computing-with-python/
𝟲. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
You'll learn how to read data from sources like CSVs and SQL, and how to use libraries like Numpy, Pandas, Matplotlib, and Seaborn to process and visualize data.
👉 https://freecodecamp.org/learn/data-analysis-with-python/
𝟳. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
You will learn how to implement the graphical representation of data in order to interactively and efficiently convey insights to clients, customers, and stakeholders in general.
👉 https://cognitiveclass.ai/courses/data-visualization-python#about-course
𝟴. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
You will build several neural networks and explore more advanced techniques like natural language processing and reinforcement learning.
👉 https://freecodecamp.org/learn/machine-learning-with-python/
9. Practice Python
👉 https://learnpython.org/
10. Free Python course by datacamp
👉 https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-1-python-basics?ex=1
ENJOY LEARNING
#Python #Programming
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26. Flask - "Lightweight web app framework."
27. Streamlit - "Create apps with minimal code."
28. Bokeh - "Interactive web-based visualization."
29. GeoPandas - "Geospatial data analysis made easy."
30. Altair - "Declarative statistical visualization."
31. Prophet - "Time series forecasting with ease."
32. Feature-engine - "Feature engineering for ML."
33. Dask - "Parallel computing for big data."
34. Vaex - "Efficient dataframes for big data."
35. Optuna - "Automated hyperparameter tuning."
36. imbalanced-learn - "Handling imbalanced datasets."
37. Eli5 - "Interpret machine learning models."
38. SHAP - "Explainability for ML models."
39. scikit-image - "Image processing in Python."
40. TextBlob - "Text processing and sentiment analysis."
41. Polars - "Fast DataFrame library."
42. Cufflinks - "Combines Plotly with pandas."
43. TA-Lib - "Technical analysis for financial data."
44. OpenCV - "Computer vision and image processing."
45. Pymc3 - "Probabilistic programming for Bayesian analysis."
46. Scrapy - "Web scraping toolkit."
47. PySpark - "Apache Spark for big data processing."
48. PyArrow - "Columnar data format for analytics."
49. OptimalFlow - "AutoML for data scientists."
50. Pycaret - "Automated machine learning toolkit."
These libraries cover a wide range of data science tasks, from data manipulation and visualisation to machine learning and deep learning, making them essential tools for any data scientist or Python programmer.
Here's a list of 25 Python libraries for data science👇
1. NumPy - "Handles arrays and math operations efficiently."
2. pandas - "Data manipulation made easy with data frames."
3. Matplotlib - "Plots and charts for data visualization."
4. Seaborn - "Creates attractive statistical plots."
5. SciPy - "Scientific and technical computing toolkit."
6. scikit-learn - "Machine learning at your fingertips."
7. TensorFlow - "For deep learning and neural networks."
8. Keras - "High-level deep learning API."
9. PyTorch - "Deep learning framework for researchers."
10. Statsmodels - "Statistical models and tests."
11. NLTK - "Natural language processing toolkit."
12. Gensim - "Topic modeling and document similarity."
13. XGBoost - "Gradient boosting for better predictions."
14. LightGBM - "Efficient gradient boosting framework."
15. CatBoost - "Optimized gradient boosting for categories."
16. NetworkX - "Build and analyze networks and graphs."
17. Beautiful Soup - "HTML and XML parsing made simple."
18. Requests - "Effortless HTTP requests."
19. SQLAlchemy - "Relational database interactions."
20. Pandas Profiling - "Generate data reports quickly."
21. Featuretools - "Automated feature engineering."
22. H2O - "Open-source machine learning platform."
23. Yellowbrick - "Visualize machine learning results."
24. Plotly - "Interactive and shareable plots."
25. Dash - "Build web apps for data visualization."
These libraries cover a wide range of data science tasks, from data manipulation and visualisation to machine learning and deep learning, making them essential tools for any data scientist or Python programmer.