Python + AI Entrepreneurship Roadmap
Stage 1 – Identify AI Opportunity (Solve Real-World Problems)
Stage 2 – Build Python/AI Skills (ML, Deep Learning)
Stage 3 – Design AI Product (Prototyping with Flask/TensorFlow)
Stage 4 – Validate AI Model (Data Collection & Training)
Stage 5 – Build MVP (Deploy AI App)
Stage 6 – Secure Funding (Pitch to Investors)
Stage 7 – Marketing & Growth (AI-Driven Campaigns)
Stage 8 – Scale Product (Optimize & Automate)
🏆 – Python AI Entrepreneur
Stage 1 – Identify AI Opportunity (Solve Real-World Problems)
Stage 2 – Build Python/AI Skills (ML, Deep Learning)
Stage 3 – Design AI Product (Prototyping with Flask/TensorFlow)
Stage 4 – Validate AI Model (Data Collection & Training)
Stage 5 – Build MVP (Deploy AI App)
Stage 6 – Secure Funding (Pitch to Investors)
Stage 7 – Marketing & Growth (AI-Driven Campaigns)
Stage 8 – Scale Product (Optimize & Automate)
🏆 – Python AI Entrepreneur
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Exclusion from the queue
The collections.deque() class is a generalization of stacks and queues, and represents a deque. A deque() supports thread-safe, memory-efficient operations for inserting and removing elements of a sequence from either side, with roughly the same O(1) performance in either direction.
The collections.deque() class is a generalization of stacks and queues, and represents a deque. A deque() supports thread-safe, memory-efficient operations for inserting and removing elements of a sequence from either side, with roughly the same O(1) performance in either direction.
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gui_calender.py
1.3 KB
GUI Calender in Python 📅
Do not forget to React ❤️ to this Message for More Content Like this
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Creating Virtual Environment for Python
» Download Python
» Steps to create '
1. Navigate to the folder where you want to make your project
Example:
2. Open terminal (local terminal, command prompt, or vs code terminal) in that folder
3. Now, use these commands
4. Your virtual environment is created in that folder, now activate this virtual environment using this command.
Command for 'Command Prompt':
Command for 'Powershell':
Command for Git Bash or WSL:
If Powershell gives you error like
5. Congratulations🎊 Your virtual environment activated now make your project
Happy Coding 👨💻
» Download Python
First you need python installed in your local machine to create virtual environment.
Download Python from Here
» Steps to create '
.env' folder (virtual environment for python)1. Navigate to the folder where you want to make your project
Example:
cd D:/code/
2. Open terminal (local terminal, command prompt, or vs code terminal) in that folder
3. Now, use these commands
python --version # Type this and hit enter to verify the python version
# Now use these commands
python -m venv .env
4. Your virtual environment is created in that folder, now activate this virtual environment using this command.
Command for 'Command Prompt':
.\env\Scripts\activate
Command for 'Powershell':
.\env\Scripts\Activate.ps1
Command for Git Bash or WSL:
source \.env\bin\activate
If Powershell gives you error like
File cannot be loaded because running scripts is disabled then use this command!Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
5. Congratulations🎊 Your virtual environment activated now make your project
Happy Coding 👨💻
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from turtle import *
import turtle as t
def my_turtle():
# Choices
sides = str(3)
loops = str(450)
pen = 1
for i in range(int(loops)):
forward(i * 2/int(sides) + i)
left(360/int(sides) + .350)
hideturtle()
pensize(pen)
speed(30)
my_turtle()
t.done()👍9👎1
Scrap Image from google using BeautifulSoup
import requests
from bs4 import BeautifulSoup as BSP
def get_image_urls(search_query):
url = f"https://www.google.com/search?q={search_query}&tbm=isch"
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
}
rss = requests.get(url, headers=headers)
soup = BSP(rss.content, "html.parser")
all_img = []
for img in soup.find_all('img'):
src = img['src']
if not src.endswith("gif"):
all_img.append(src)
return all_img
print(get_image_urls("boy"))
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📈 Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
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