Neha Mishra’s Post

🚀 Why Python is the Backbone of Data & AI (My Practical Understanding) Most beginners learn Python as just a programming language. But in reality, Python is a complete problem-solving ecosystem. 💡 Here’s how I see it (my practical understanding): ✔ Data Analysis → Pandas ✔ Numerical Computing → NumPy ✔ Data Visualization → Matplotlib / Seaborn ✔ Machine Learning → Scikit-learn ✔ AI / Deep Learning → TensorFlow, PyTorch ⚙️ What makes Python powerful? • Simple and readable syntax → faster development • Multi-paradigm support → flexible problem-solving • Massive library ecosystem → ready-to-use solutions 🔍 Technical Insight (Important): Python is not just an interpreted language. It first converts code into bytecode, which is then executed by the Python Virtual Machine (PVM) — making it platform-independent. #Python #DataAnalytics #AI #MachineLearning #CareerGrowth #TechSkills

Continuing my journey learning Python for AI/ML, I built a web scraper that collects quotes and author details from a website. GitHub Repo - https://lnkd.in/gej-ZwFG What the scraper does: • Scrapes quotes from multiple pages • Extracts tags associated with each quote • Visits author pages to collect birth date and location • Uses caching to avoid repeated requests for the same author • Saves everything into a structured JSON dataset While building this I practiced concepts like: pagination scraping, nested HTML parsing, multi-page data extraction and optimizing requests. Next step: building more advanced scrapers and exploring data collection for real-world datasets. Learning by building 🚀 #python #webscraping #AI #BuildInPublic #Learning

Python opens multiple paths Pick one based on your goal Start here https://lnkd.in/dBMXaiCv https://lnkd.in/dtFbRP96 Data • Python + Pandas Clean data Analyze data Next step • Python + Matplotlib Visualize results Machine learning • Python + Scikit-learn Train models Predict outcomes Deep learning • Python + TensorFlow Build neural networks Web • Python + Flask Build APIs Create web apps Games • Python + Pygame Build simple games Mobile • Python + Kivy Create mobile apps Advanced charts • Python + Seaborn Better visual insights Simple rule Pick one path Build 3 projects Then switch Example Data path • Clean dataset • Build dashboard • Predict trends Web path • Build API • Connect database • Deploy app If you learn everything You master nothing Question Which path are you choosing #Python #Programming #DataScience #ProgrammingValley

Python is the backbone of most AI systems. Models, data, APIs and automation, most of it runs through Python. That is why the ecosystem matters more than the syntax. Different parts of AI rely on different tools: • Data prep → Pandas • Model building → TensorFlow • Visualization → Matplotlib / Seaborn • Data collection → BeautifulSoup / Selenium • Serving models → FastAPI / Flask • Full systems → Django • Vision tasks → OpenCV AI is a pipeline. And Python sits across that entire pipeline. If you understand how these pieces connect, you move from scripts to systems. Which part of the AI workflow are you focusing on right now? 👉 Built an AI tool? Get it featured in our community of 13M+ AI Professionals: https://lnkd.in/gRjpdKYx Graphic credits to respective owner. #ai #python #machinelearning #datascience #generativeai

I am working on an AI project using Python and Flask, calling Claude Opus/Sonnet or OpenAi. Below is a very useful guide to AI tools.

Python isn’t just a programming language anymore — it’s the foundation of modern AI. From data manipulation with Pandas to deep learning with TensorFlow, from visualization using Matplotlib and Seaborn to deploying APIs with FastAPI — Python sits at the center of the entire AI ecosystem. What makes Python so powerful isn’t just its simplicity, but its ecosystem: • Data → Pandas • ML/AI → TensorFlow • Visualization → Matplotlib, Seaborn • Automation → Selenium, BeautifulSoup • Backend → Flask, Django, FastAPI • Databases → SQLAlchemy Whether you're building intelligent systems, automating workflows, or creating scalable platforms — Python is the common thread tying it all together. #Python #ArtificialIntelligence #MachineLearning #DataScience #GenAI #Technology #Learning P.s. credits to the original uploader for the infographic.

I’ve been building a machine learning–based approach to extract data from engineering graphs. 📊 The goal is to take graph images (like pressure vs depth) and convert them into structured, usable data instead of relying on manual digitization. I developed a Python pipeline using OpenCV and explored ML-based approaches to improve how curves are detected and separated — including experimenting with U-Net for segmentation and a CNN-based model for prediction.🤖🧠 One of the more challenging parts was getting consistent curve detection and accurately mapping pixel values to real-world units. It took quite a bit of iteration to get the extracted output to closely match the original graph behavior. On the left is the Original Graph, and on the right is the extracted output. I’m really happy with how it’s coming together so far, especially working on something that connects machine learning with a practical, real-world use case.🚀 Tools used: Python, OpenCV, NumPy, Pandas, CNN, U-Net 💻 Sharing a snapshot of the output below 👇 #MachineLearning #DataAnalytics #ComputerVision #Python

Breaking Python into AI/ML can feel overwhelming — so many tools, libraries, and concepts. Here’s a simple mindset shift that working for me Instead of going bottom-up (learning everything in depth first), try a Top-Down approach: - Start with a small MVP project - Use basic Python + a few key libraries - Don’t aim for perfection — aim to build something Then iterate: Learn NumPy & Pandas while handling data. Use Matplotlib / Seaborn for quick insights Apply scikit-learn for basic ML models Gradually understand what’s happening under the hood 💡 The trick is selective learning — You don’t need to master everything before starting. Build → Break → Learn → Improve → Repeat Over time, you’ll realize: What once looked complex becomes your foundation. That’s how real learning compounds. #Python #AI #MachineLearning #DataScience #LearningPath #MVP #CareerGrowth #SelfLearning #SelfGrowth

🚀 Why is Python ruling Data Science & AI? Because it’s simple, powerful, and gets the job done faster. From handling huge data with ease to building smart AI models, Python makes complex work feel easy. With tools like NumPy, Pandas, and TensorFlow, developers can create powerful solutions without wasting time on complicated code. 💡 Whether it’s AI, automation, or web apps—Python does it all. That’s why businesses trust it to innovate and grow faster. 👉 Want to build smarter solutions? Start with Python. For more information, please read https://lnkd.in/ggjJDWrb #python #datascience #artificialintelligence #machinelearning #ai #tech #programming #innovation #automation #businessgrowth

🚀 Day 4 of My Generative & Agentic AI Journey! Today’s focus was on one of the most commonly used data types in Python — Strings. Here’s what I learned: 🔤 Strings in Python: • Strings are immutable — once created, they cannot be changed • Any modification creates a new string instead of changing the original 🔍 Indexing & Slicing: • Accessing individual characters using indexing • Extracting parts of a string using slicing • Learned how powerful slicing is for handling text data 🔐 Encoding & Decoding: • Understood how strings are converted into bytes (encoding) • And how bytes are converted back to strings (decoding) • Important for handling data, APIs, and real-world applications 👉 Key takeaway: Strings are everywhere — from user input to AI models — and understanding how to work with them efficiently is a must. Another step closer to mastering the fundamentals 💪 #Day4 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic