Why Python excels in machine learning

I keep wondering… why is almost every AI tool built on Python? It doesn’t really make sense at first. C++ is faster Rust is safer Java is built for scale So why did Python win? The answer is surprisingly simple. Because AI isn’t just an engineering problem. It’s an experimentation problem. When you’re building models, you’re not optimizing code first. You’re trying ideas. Breaking things. Testing again. Iterating constantly. Python just makes that easy. Less boilerplate Faster to write Easier to read A massive ecosystem ready to plug into And here’s the part most people miss. When you run an AI model, Python isn’t doing the heavy lifting. Underneath, it’s all highly optimized C++, CUDA, and hardware acceleration. Python is just the glue that holds everything together. So in a way, Python didn’t win because it’s the fastest. It won because it gets out of your way. And maybe that’s the bigger lesson beyond AI. Sometimes the best technology isn’t the most powerful one. It’s the one that lets more people build, faster. Curious how you see it. Do you think Python will still dominate AI in the long run, or are we heading toward something else? #ArtificialIntelligence #Python #MachineLearning #DataScience #SoftwareEngineering #TechLeadership #Innovation #AI #Programming #FutureOfWork

🚀 Day 22 of My Generative & Agentic AI Journey! Today’s focus was on Comprehensions in Python — a concise and powerful way to create collections using a single line of code. Here’s what I learned: ⚡ Comprehensions in Python: • Used to create lists, sets, dictionaries, and even generators • Help write logic in a compact and readable way 🧠 Where are they used in real life? • Filtering items → Selecting specific elements from data • Transforming items → Modifying data while creating a new collection • Creating new collections → Generating lists, sets, or dictionaries efficiently • Flattening nested structures → Converting nested data into a single structure 🎯 Purpose of Comprehensions: • Cleaner code → Less lines, more readability • Faster execution → More optimized than traditional loops 💡 Key takeaway: Comprehensions make Python code more elegant and efficient — a must-know concept for writing professional-level code. Moving one step closer to writing optimized and clean Python 🚀 #Day22 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

AI writes Python code... backwards. I noticed something that kept bugging me: AI coding assistants like Claude Code most often generate Python functions in bottom-up order, with helpers first and entry points last. What's the deal? This forces you to read code from the bottom up to understand what it does. The exact opposite of how we naturally read, from the headline to the supporting details, like a newspaper! So I built 🔧 flake8-stepdown, a tool that detects and auto-fixes function ordering violations. It works as a flake8 plugin and as a standalone CLI. I wrote a blog post to explain how it works! 💡 Don't just prompt AI to "write better code", constrain it with deterministic tools (linter, auto-formatter, ...) Links in the comment below 👇 #Python #AI #CodeQuality #DeveloperTools #OpenSource

Which Python do you know in 2026? 🐍 Most people say they “know Python”…but in reality, they only know the basics. Today, Python is not just a programming language it’s a complete ecosystem. From data analysis (pandas, Polars) to machine learning (scikit-learn, PyTorch), from big data (PySpark) to AI & LLM apps (Hugging Face, LangChain, LlamaIndex) your growth depends on the tools you use with Python. Want to build dashboards? → Streamlit Want to scale systems? → Ray, Dask Want to manage pipelines? → Prefect Want clean projects? → Poetry 👉 The difference between an average developer and a high-value professional is tool awareness + real-world usage. Don’t just learn Python, Learn what to build with Python. 📌 Start small → Pick one tool → Build projects → Stay consistent. So tell me 👇 Which of these tools have you already used? And what are you learning next? #Python #DataAnalytics #DataScience #AI #MachineLearning #CareerGrowth

Learning Python the right way isn’t about memorizing syntax. It’s about understanding how to think, solve problems, and build systems. Recently, I’ve been diving into Head First Python — a practical, hands-on approach to mastering programming fundamentals. What stands out: Focus on real-world application, not just theory Strong emphasis on problem-solving and data handling Learning by doing — building, experimenting, and iterating One key takeaway: 👉 Programming is not about knowing everything 👉 It’s about knowing how to figure things out In today’s AI-driven world, combining Python + AI + problem-solving skills creates massive leverage. Because tools will change. But thinking frameworks stay. This is just part of the journey — building deeper technical skills and applying them to real-world problems. #Python #Programming #Learning #AI #SoftwareDevelopment #TechSkills #Developers #CareerGrowth #ProblemSolving

🚀 Day 1: Python Basics for Gen AI Revision – The Foundation! Stepping into my "Python – Gen AI Revision" journey today with a sharp focus: Mastering the core fundamentals required for Generative AI development and aiming for a role in an MNC within 90 days. It’s easy to get excited about LLMs and Diffusion models, but without a rock-solid Python foundation, those complex structures can't stand. That's why Day 1 is dedicated to the core. 🧠 What I Re-covered/Focused On Today: PEP 8 Standards & Syntax: Emphasizing readable, professional code structure from the start. Essential Data Types & Flow Control: Revisiting loops, if/else logic, and efficient variable management. Advanced Fundamentals: Getting hands-on practice with lambda functions, list comprehensions, and proper docstring usage—critical for real-world development. I’ve compiled all concepts, code examples, and best-practice notes into a comprehensive Google Colab Notebook and pushed it to my new repository: python-genai-journey. This isn't just theory; it’s about preparing myself to write industry-standard Python for the future of AI. 💻 Check my progress & the code here: 🔗 https://lnkd.in/gUfc6Ky6 One day down, many more to go. Follow along as I build my way to a Gen AI career! #Python #GenAI #GenerativeAI #100DaysOfCode #AIDevelopment #TechJourney #MNCGoal #RevisionSeries

New article: Sample Paths for Uncertainty Quantification in Time Series Forecasting In this article, we explore the difference between marginal and joint distributions, and how they answer different questions when quantifying uncertainty in time series forecasting. Plus, we get a hands-on experiment with the latest release of #neuralforecast which now supports sample paths across all models. Enjoy the read! #timeseries #forecasting #deeplearning #machinelearning #python #artificialintelligence https://lnkd.in/ekrZPn8S

⭐️ An insightful article on uncertainty quantification in time series forecasting, featuring the latest #neuralforecast 🧠 release. Check it out 👇

Day 5 of the AI & Data Analysis Challenge ✨❤️ سؤال بسيط في Python لكنه مهم لأي حد شغال في Data Analysis. When we use *args in a function, where are the values stored? 📌 Options: 1)Tuple 2)List 3)Set 4)String ✅ Answer: Tuple Explanation: When we use *args in Python, the function can accept a variable number of arguments. Python automatically collects these values into a tuple. Example: Python def numbers(*args): print(type(args)) numbers(1, 2, 3, 4) Output: <class 'tuple'> 💡 Why does Python use a tuple? Immutable (values cannot be changed) Faster than lists Safe for function arguments Did you know this before? 👀 #Python #DataAnalysis #AI #LearningInPublic #DataScience Rawan Mahmoud Mariam Ghareeb