Building Collaborative Filtering Recommendation Engine with Python

Linear Regression: Orange vs Python To perform Linear Regression on a dataset, the first step is to clearly identify the features (independent variables) and the target (dependent variable). The model learns how the target variable is influenced by one or more features. Once the data is prepared: The model is fitted on the dataset It is then trained and tested (typically using a train-test split) Predictions are generated, allowing comparison between actual values and predicted values To evaluate the model’s performance, key metrics such as: R² Score (explains how well the model captures variance) Mean Absolute Error (MAE) (more appropriate here than MSE if that’s what you intended) are calculated. I implemented Linear Regression using both Orange (a visual, no-code tool) and Python (code-based approach) on the same dataset. Interestingly, the results from both approaches were almost identical, with only negligible differences. This highlights an important insight: The underlying mathematics and algorithms remain the same, regardless of whether you use a visual tool like Orange or write code in Python. The difference lies mainly in ease of use, flexibility, and control, not in the core outcomes. #ArtificialIntelligence #MachineLearning #DeepLearning #DataScience #AI #GenerativeAI #Automation #FutureOfWork #Learning #Education #EdTech #LifelongLearning #SkillDevelopment

Machine Learning Image Data using imutils #machinelearning #datascience #imagedata #imutils A series of convenience functions to make basic image processing functions such as translation, rotation, resizing, skeletonization, displaying Matplotlib image easier with OpenCV and both Python 2.7 and Python 3.0 https://lnkd.in/gzUw7kQm

The Python ecosystem at a glance - proof that Python's real strength lies in its libraries, letting one language stretch across wildly different domains. - pandas - Data wrangling and analysis - scikit-learn - Machine learning models and pipelines - TensorFlow - Deep learning and neural networks - Matplotlib - Charts and data visualization - Seaborn - Statistical and advanced plotting - BeautifulSoup - Web scraping and HTML parsing - Selenium - Browser automation and testing - FastAPI - High-performance APIs - SQLAlchemy - Database access and ORM - Flask - Lightweight web apps - Django - Full-scale web platforms - OpenCV - Computer vision - Pygame - Game development Python on its own is simple. But when paired with the right library is a specialist tool for nearly any field. #Python #MachineLearning #DataScience

Day 9/20 of my AI/ML journey 🚀 Today I explored web scraping and why it matters for machine learning. A lot of people assume datasets magically exist somewhere ready for analysis. In reality, data often has to be collected first. Web scraping makes that possible. Using Python, you can extract information from websites and convert it into structured data that can be analyzed later. This opens the door to building your own datasets instead of relying only on pre-packaged ones. No data → no machine learning. #AGIT20DayChallenge #LearningInPublic #AI #MachineLearning #WebScraping #Python

Machine Learning Frameworks using pyriemann #machinelearning #datascience #machinelearningframeworks #pyriemann pyRiemann is a Python package for machine learning with multivariate data, using the Riemannian geometry of symmetric (resp. Hermitian) positive definite (SPD) (resp. HPD) matrices. It provides a high-level interface fully compatible with scikit-learn, making it easy to build powerful pipelines for classification, regression, and clustering. https://lnkd.in/gDTXRJJu

🎬 I watched a video so you don't have to. Here's the executive summary: "⚡️Monty: the ultrafast Python interpreter by Agent..." Monty is an ultrafast Python interpreter designed for AI agents, delivering massive startup and runtime speed while preserving Python semantics. 🔑 Key Insights: → Monty targets agent workloads to reduce cold-start and per-call overhead, enabling faster decision loops. → Compatibility with existing Python code is prioritized; but some edge-case features may have limited support. → Adoption hinges on measurable gains in common agent pipelines and a simple integration path into current stacks. 💎 Best quote: "Monty is an ultrafast Python interpreter for agents" ⏱️ Time saved: 30 minutes — Full breakdown in my first comment 👇 #AI #VideoAnalysis #Productivity #Steek

Today I open-sourced a Python library that lets AI generate functions at runtime. I built PyFuncAI, a lightweight Python library that allows LLMs to dynamically generate and execute Python functions from natural language. Instead of writing dozens of helper utilities for an AI system, the model can generate them on demand. Some technical details: • Supports lazy or eager function generation • Caching prevents repeated LLM calls for identical prompts • Generated code is compiled and injected into the runtime I originally built this while experimenting with agentic systems, where tools often need to be created dynamically instead of predefined. GitHub: https://lnkd.in/ghQTsZcm PyPI: https://lnkd.in/gr5KaSW2 #Python #AI #OpenSource #AIAgents #DeveloperTools

Why Python For ML? Python wasn't designed for ML. But it accidentally became the king of AI. Here's the unusual story. Day 3 of 60 → Why does EVERY ML engineer use Python? Python was created in 1991 for general programming. Nobody planned it for AI. But here's what happened:  ·      scikit-learn — made ML accessible with clean APIs ·      NumPy — made fast math possible ·      pandas — made data manipulation human-readable ·      matplotlib — made visualizations easy ·      TensorFlow + PyTorch — made deep learning reachable The community built the tools. The tools built the ecosystem. The ecosystem became impossible to ignore. Today, most of the ML engineers use Python as their primary language. It's not the fastest language. It's not the most efficient. But it's the most learnable, most readable, and most supported. For ML, that's everything. If you're just starting: Python IS the answer. #Python #MachineLearning #DataScience #Programming #60DaysOfML #AI

What if you could turn any Python function into an AI-powered one with just one line of code? Marvin makes that possible. One of the biggest shifts in AI development right now is simplicity. Marvin is a lightweight library that lets you add AI capabilities to ordinary Python functions with almost no extra code. No complex pipelines. No heavy frameworks. Just natural Python. Instead of building elaborate integrations, you describe what you want the function to do, and Marvin handles the language model interaction behind the scenes. What makes it interesting: - You can turn regular functions into AI-powered ones - Minimal setup and clean syntax - Works naturally with existing Python code - Great for quick prototypes and automation tasks - Removes a lot of boilerplate around LLM calls It feels less like “using an AI framework” and more like upgrading Python itself. Tools like this are lowering the barrier to building intelligent applications. You don’t need massive architectures anymore. Sometimes one well-designed abstraction is enough. #machinelearning #ai #datascience #data

🚀 Built a Simple AI Image Analyzer using Python & Streamlit Today I built a small project where users can upload an image and ask questions about it using AI. 🔹 Technologies Used: • Python • Streamlit • Google Gemini API • PIL (Python Imaging Library) 💡 How it works: 1️⃣ Upload an image 2️⃣ Enter a prompt/question about the image 3️⃣ AI analyzes the image and generates a response This project helped me understand: Integrating Generative AI APIs Handling image inputs in Python Building simple AI web apps using Streamlit I'm currently learning and exploring Data Analytics, AI tools, and Python projects. Excited to build more practical projects! 🚀 #Python #Streamlit #GenerativeAI #GoogleGemini #AIProjects #LearningInPublic #DataAnalytics