Start learning Python step by step https://lnkd.in/dtFbRP96 Learn Python from zero https://lnkd.in/dw3T2MpH Top Python modules you should know Data and analysis NumPy Work with arrays and numbers Pandas Clean and analyze data Matplotlib Create charts and graphs Machine learning Scikit-learn Build ML models TensorFlow / PyTorch Deep learning projects NLP NLTK Process text data Web and APIs Requests Send HTTP requests BeautifulSoup Parse HTML Flask Build simple APIs Django Build full web apps Flask-RESTful Create REST APIs Database SQLAlchemy Work with databases Automation and background jobs Celery Run async tasks Testing pytest Write and run tests Environment Virtualenv Isolate dependencies GUI and apps Tkinter Desktop apps Pygame Game development Images and parsing Pillow Edit images Requests-HTML Parse modern websites Advanced Twisted Event-driven networking Ask yourself Which of these have you actually used Pick 2 Build a small project today Save this Come back later More content https://lnkd.in/dBMXaiCv #Python #MachineLearning #DataScience #Programming #ProgrammingValley

I built a RAG-based AI assistant that learns from 100 Python tutorial videos. This AI can answer any Python question and also tells you which video and which part of the video explains that concept. The dataset comes from the “100 Days of Python” playlist by Code With Harry. Shoutout to https://lnkd.in/gqKfE74b A big thanks to him for creating such an amazing learning resource. 🙌 GitHub Repository: https://lnkd.in/g2eJJeyM Most chatbots give generic answers. But this RAG-based system is designed specifically for Python beginners, helping them learn directly from structured video tutorials and providing a clearer Python learning roadmap. Tech Stack Used Frontend React React Markdown Backend FastAPI Vector Database Embedding Model Ollama Whisper Model - large v2 Llama 3.2 How It Works Step 1 – Collect Videos Download and store all tutorial videos in the videos folder. Step 2 – Convert Videos to MP3 using FFmpeg Extract audio from each video by running the video_to_mp3 script. Step 3 – Convert MP3 to JSON using Whisper Transcribe the audio using the Whisper model and save the output as JSON files. Step 4 – Convert JSON to Vector Embeddings Use the bge-m3 embedding model with Ollama to convert transcripts into vector embeddings. The preprocess_json script converts the JSON data into a dataframe and stores it as a joblib file. Step 5 – Query Processing with the LLM When a user asks a question: The system loads the embeddings from the joblib file Retrieves the most relevant transcript segments Generates a contextual prompt Sends the prompt to Llama 3.2 via Ollama The AI returns the answer along with the relevant video reference I learned a lot while building this project, especially about RAG pipelines, embeddings, and building AI-powered learning tools. Feedback is welcome! 🚀 #AI #MachineLearning #RAG #GenerativeAI #Python #LLM #ArtificialIntelligence

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 isn’t “just a language” It’s an entire ecosystem 👇 Data Python + Pandas → Clean & transform data Python + Matplotlib / Seaborn → Tell stories with data AI Python + Scikit-learn → Build ML models Python + TensorFlow → Go deep with neural networks Python + OpenCV → Power computer vision Backend Python + Django → Scale products Python + Flask → Ship fast Python + FastAPI → Build blazing APIs Python + SQLAlchemy → Handle your database Automation Python + BeautifulSoup → Scrape the web Python + Selenium → Automate browsers Creative Python + Pygame → Build games What are you building with Python?

🚀 Day 26 – The 30-Day AI & Analytics Sprint Python supports multiple inheritance, which allows a class to inherit from multiple parent classes. However, this can create ambiguity in method resolution. Question? 🔍Explain: 1)What is MRO (Method Resolution Order) in Python? 2)How does Python decide which parent method to call first? 3)Why does Python use the C3 Linearization algorithm? 4)Give a real example where multiple inheritance may cause confusion. ✅Answers 1️⃣(Method Resolution Order)? MRO (Method Resolution Order) is the order in which Python searches for a method or attribute in a class hierarchy when a method is called. 2️⃣How does Python decide which parent method to call first? Python follows the MRO list to determine the order of method lookup. 3️⃣ Why does Python use the C3 Linearization algorithm? Python uses the C3 Linearization algorithm to compute the MRO. This algorithm ensures: Consistency in the order of method resolution Preservation of the inheritance hierarchy No conflicts in complex multiple inheritance structures The C3 algorithm guarantees that the method search order is logical, predictable, and conflict-free. 4️⃣Example A common confusion occurs in the Diamond Problem, where two classes inherit from the same parent class: class A:   def show(self):     print("A") class B(A):   def show(self):     print("B") class C(A):   def show(self):     print("C") class D(B, C):   pass obj = D() obj.show() Python resolves this using MRO: D → B → C → A → object So the program calls B.show(). 👌Pro Tip: Use ClassName.mro() to debug your inheritance tree and avoid unexpected bugs! 🙏Thanks for: Muhammed Al Reay , Mariam Metawe'e and Instant Software Solutions #Python #OOP #MachineLearning #AI #DataScience #Programming #Analytics

Day:1 🔥 Most developers still initialize Python variables like it’s 2015… But AI just changed the game. 👇 🧑💻 Traditional Python user_level = int(input("Enter level: ")) if user_level < 1: user_level = 1 Manual rules. Repeated validation. Static logic. 🤖 Python + AI from openai import OpenAI client = OpenAI() response = client.responses.create( model="gpt-4.1-mini", input="Suggest a default skill level for a new coding student" ) user_level = int(response.output_text.strip()) Now your variables are: ✅ Context-aware ✅ Intelligent ✅ Automatically optimized 💡 AI isn’t replacing Python — it’s upgrading how we write it. Small change → Smarter applications → Better developer productivity. Are you still initializing variables manually or letting AI assist you? 👇 #Python #AI #Developers #Programming #TechInnovation #MachineLearning #Coding

🚀 Excited to share my latest project – a **Python Voice Assistant**! I recently built a simple voice assistant using Python that can understand voice commands and perform useful tasks. 🔧 **Key Features:** • Recognizes voice commands • Converts text to speech responses • Plays songs on YouTube • Searches Google • Fetches information from Wikipedia • Tells the current time 🛠 **Technologies Used:** Python, SpeechRecognition, pyttsx3, pywhatkit, Wikipedia API This project helped me explore **speech recognition, automation, and integrating different Python libraries** to build a smart assistant. 📂 GitHub Repository: https://lnkd.in/gJcJ73dY I’m continuously improving this assistant and planning to add more features like **weather updates, system control, and AI responses**. Would love to hear your feedback and suggestions! 😊 #Python #VoiceAssistant #Programming #SoftwareDevelopment #LearningJourney #AI #TechProjects

Most people start by learning Python… but what really matters is what you build with it. Python isn’t just a programming language — it’s an entire ecosystem that opens doors to multiple domains. With the right tools, you can move from: 📊 analyzing data → 🤖 building AI → 🌐 creating real-world applications Here’s what you can unlock: 🔹 Pandas → Clean, analyze, and manipulate data 🔹 NumPy → Perform fast numerical computations 🔹 Scikit-learn → Build machine learning models 🔹 TensorFlow → Create deep learning systems 🔹 Matplotlib & Seaborn → Turn data into visual insights 🔹 Flask → Develop web apps and APIs 🔹 Pygame → Build interactive games 🔹 Kivy & Tkinter → Create desktop and mobile apps 💡 Python isn’t just something to learn. It’s something to build with. #Python #PythonProgramming #LearnPython #PythonDeveloper #Coding #Programming #SoftwareDevelopment #DataScience #MachineLearning #DeepLearning #AI #ArtificialIntelligence #DataAnalytics #Tech #Technology #Developers #CodingLife #ProgrammingLife #WebDevelopment #AppDevelopment #GameDevelopment #100DaysOfCode #CodeNewbie #TechEducation

🚀 Day 6 of My Generative & Agentic AI Journey! Today’s focus was on Lists in Python — one of the most commonly used and powerful data structures. Here’s what I learned: 📋 Lists in Python: • Similar to arrays in other programming languages • Lists are mutable — meaning they can be changed after creation ⚙️ Common List Operations: • append() → Add element at the end Example: [1, 2, 3, 4, 5] → append(6) → [1, 2, 3, 4, 5, 6] • remove() → Remove a specific element Example: remove(3) • extend() → Merge two lists Example: [1, 2] + [3, 4] → [1, 2, 3, 4] • insert() → Insert element at a specific position • pop() → Remove element using index • min() / max() → Find smallest and largest values • sort() → Sort the list • reverse() → Reverse the list 👉 Key takeaway: Lists are super flexible and essential for handling collections of data in real-world applications. Learning how to manipulate data efficiently step by step 💪 #Day6 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic