Python | Machine Learning | Coding

Python | Machine Learning | Coding | R

🖥 Free Courses on Large Language Models

▪ChatGPT Prompt Engineering for Developers

▪LangChain for LLM Application Development

▪Building Systems with the ChatGPT API

▪Google Cloud Generative AI Learning Path

▪Introduction to Large Language Models with Google Cloud

▪LLM University

▪Full Stack LLM Bootcamp

✅ More ♥️♥️ = more posts

@CodeProgrammer ♥️

4.5K viewsedited 12:58

Python | Machine Learning | Coding | R

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The source code for DragGAN has been released! 🔥🔥🔥

We can finally play with that marvel!

🔗 GitHub repository: https://github.com/XingangPan/DragGAN

https://t.me/DataScienceT

4.5K views20:04

Python | Machine Learning | Coding | R

A special and important channel to download the most important books to learn programming and data science

t.me/DataScienceM

Data Science Machine Learning Data Analysis Books

This channel is for Programmers, Coders, Software Engineers.

1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning

Cross promotion and ads: @hussein_sheikho

4.2K views06:35

Python | Machine Learning | Coding | R

How to Make a Twitter Bot in Python With Tweepy

https://realpython.com/twitter-bot-python-tweepy/

✅ More ♥️♥️ = more posts

@CodeProgrammer ♥️

4.4K viewsedited 07:35

Python | Machine Learning | Coding | R

Understanding Simple Recurrent Neural Networks in Keras

https://machinelearningmastery.com/understanding-simple-recurrent-neural-networks-in-keras/

✅ More ♥️♥️ = more posts

@CodeProgrammer ♥️

4.9K viewsedited 17:03

Python | Machine Learning | Coding | R

Python Docker Tutorials

Docker is a containerization tool used for spinning up isolated, reproducible application environments. It is a popular development tool for Python developers.

https://realpython.com/tutorials/docker/

5.0K views06:57

Python | Machine Learning | Coding | R

The Transformer Attention Mechanism

https://machinelearningmastery.com/the-transformer-attention-mechanism/

✅ More ♥️♥️ = more posts

@CodeProgrammer ♥️

4.9K viewsedited 03:30

Python | Machine Learning | Coding | R

5.1K views03:50

Python | Machine Learning | Coding | R

Data Version Control With Python and DVC

https://realpython.com/python-data-version-control

@CodeProgrammer ♥️

5.0K viewsedited 05:49

Python | Machine Learning | Coding | R

The Luong Attention Mechanism

https://machinelearningmastery.com/the-luong-attention-mechanism/

@CodeProgrammer

4.7K viewsedited 18:20

Python | Machine Learning | Coding | R

Forwarded from Python | Machine Learning | Coding | R

This channels is for Programmers, Coders, Software Engineers.

0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages

https://t.me/addlist/8_rRW2scgfRhOTc0

3.4K views20:28

Python | Machine Learning | Coding | R

Forwarded from Data Science | Machine Learning with Python for Researchers

🖥 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

https://t.me/DataScienceT

4.5K views07:20

Python | Machine Learning | Coding | R

🖥 Unraveling the Magic of Sorting: A Python Guide for Novices

▪Bubble Sort

def bubble_sort(list):
    for i in range(len(list)):
        for j in range(len(list) - 1):
            if list[j] > list[j + 1]:
                list[j], list[j + 1] = list[j + 1], list[j] # swap
    return list

▪Selection Sort

def selection_sort(list):
    for i in range(len(list)):
        min_index = i
        for j in range(i + 1, len(list)):
            if list[min_index] > list[j]:
                min_index = j
        list[i], list[min_index] = list[min_index], list[i] # swap
    return list

▪Insertion Sort

def insertion_sort(list):
    for i in range(1, len(list)):
        key = list[i]
        j = i - 1
        while j >=0 and key < list[j] :
            list[j+1] = list[j]
            j -= 1
        list[j+1] = key
    return list

▪Quick Sort

def partition(array, low, high):
    i = (low-1)
    pivot = array[high]

    for j in range(low, high):
        if array[j] <= pivot:
            i = i+1
            array[i], array[j] = array[j], array[i]
    array[i+1], array[high] = array[high], array[i+1]
    return (i+1)

def quick_sort(array, low, high):
    if len(array) == 1:
        return array
    if low < high:
        partition_index = partition(array, low, high)
        quick_sort(array, low, partition_index-1)
        quick_sort(array, partition_index+1, high)

https://t.me/CodeProgrammer

5.3K viewsedited 11:13

Python | Machine Learning | Coding | R

NumPy Tutorial: Your First Steps Into Data Science in Python

https://realpython.com/numpy-tutorial

https://t.me/CodeProgrammer

5.3K viewsedited 10:28

Python | Machine Learning | Coding | R

Building an Image Recognition API using Flask.

Step 1: Set up the project environment

1. Create a new directory for your project and navigate to it.
2. Create a virtual environment (optional but recommended):
(Image 1.)
3. Install the necessary libraries (image 2.)

Step 2: Create a Flask Web Application
Create a new file called app.py in the project directory (image 3.)

Step 3: Launch the Flask Application
Save the changes and run the Flask application (image 4.)

Step 4: Test the API
Your API is now up and running and you can send images to /predict via HTTP POST requests.
You can use tools such as curl or Postman to test the API.
• An example of using curl (image 5.)
• An example using Python queries (image 6.)

https://t.me/DataScienceT

5.4K views16:43

Python | Machine Learning | Coding | R

📩 Python Email Automation Script

import smtplib
from email.mime.text import MIMEText


sender_email = "your_email@example.com"
recipient_email = "recipient_email@example.com"

subject = "Automated Email"
message = "This is an automated email sent using Python."


# SMTP server configuration (example: Gmail)


smtp_server = "smtp.gmail.com"
smtp_port = 587
smtp_username = "your_username"
smtp_password = "your_password"



msg = MIMEText(message)
msg["Subject"] = subject
msg["From"] = sender_email
msg["To"] = recipient_email
try:

    server = smtplib.SMTP(smtp_server, smtp_port)
    server.starttls()
       server.login(smtp_username, smtp_password)
    server.sendmail(sender_email, recipient_email, msg.as_string())
    print("Email sent successfully!")
    
except Exception as e:
    print("Error sending email:", str(e))
    
finally:

    server.quit()

https://t.me/DataScienceT

5.5K views03:24

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