Asomugha Grace’s Post

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

🐍 Most beginners think Python learning starts with AI… but the real power begins with mastering the basics. Before building AI models or data science projects, you must understand the core Python building blocks. This week I explored Session 4 of Python learning, and here are some key takeaways every beginner should know 👇 💡 What I Learned in Python (Session 4) 1️⃣ Python as a Powerful Programming Language Python is a high-level, interpreted language known for readability, portability, and strong library support. It supports procedural, object-oriented, and functional programming, making it extremely versatile. ⚡ That’s why Python powers fields like: ✓AI & Machine Learning ✓Data Analysis ✓Web Development ✓Automation 2️⃣ Lists – The Most Used Data Structure Lists store multiple values in a single variable. Example: fruits = ["Apple", "Mango", "Banana"] print(fruits) Common operations: ✔ Add elements ✔ Remove elements ✔ Sort items ✔ Find length Lists are essential when working with datasets and collections of data. 3️⃣ For Loops – Automating Repetitive Tasks Loops allow Python to repeat actions efficiently. Example: for i in range(5): print(i) You can also loop through lists: for fruit in fruits: print(fruit) This is fundamental for data processing and automation scripts. 4️⃣ Functions – Writing Reusable Code Functions make programs modular and reusable. Example: def greet(name): print("Hello", name) greet("Python Learner") Benefits: ✔ Reduces repeated code ✔ Improves readability ✔ Makes programs scalable 5️⃣ Why Python Basics Matter Before AI Many people jump directly to AI tools, but strong Python fundamentals help you: ✅ Understand algorithms ✅ Manipulate data effectively ✅ Build automation tools ✅ Develop scalable applications Master the basics → then AI becomes much easier. 🎯 My Key Learning Python is not just about AI or machine learning. It’s about logic, data structures, and problem solving. Once you understand these fundamentals, everything else becomes easier. 👇 Drop a comment ❓ What was the first Python concept you learned? ❓ Are you currently learning Python or AI? I’d love to hear your experience! #Python #PythonProgramming #CodingJourney #LearnPython #ProgrammingBasics #TechLearning #Developers #DataScience #AI #100DaysOfCode

I'm going back to learning python. 2+ years of experience coding in Python and still not confident enough? That's not it. Python is my strongest suit. The language I'm most comfortable in. But python is versatile, and also at the core of every major AI engineering project. Learning syntax, loops, classes, or even libraries like PyTorch and sklearn is not enough. That's useful in notebooks, not in production. But AI is supposed to code now! True, but we still need to validate, debug, and set up the pipelines. So this is the plan now: → Pydantic for data validation (every good engineer knows we can't trust raw inputs)  → src layouts and proper project structuring (modular code is scalable code)  → Writing code others can actually read and extend (useless if others can't use what you create) None of this is glamorous, yet it's what separates a college project from a deployed system. Resources I'm working through: • Hypermodern Python (Claudio Jolowicz): https://lnkd.in/gEcKSr5y • Pydantic Docs: https://lnkd.in/gTB8kTCT • ArjanCodes on YouTube: https://lnkd.in/gWKp3u43 I'm still building this list, so please share if you have more! And tell me, If you've shipped ML in production, what Python skill do you wish you'd learned earlier? #Python #AIEngineering #MachineLearning #DataScience

Day 13 of my 60-Day Python + AI Roadmap. 🚀 No new theory today. Just pure practice. 💪 Because reading Python ≠ writing Python. The only way to actually learn — is to solve. 5 beginner problems using everything from Day 1–12: variables · loops · if-else · operators · typecasting 🏆 Community Challenge — How many can you solve? 1️⃣ Even or Odd checker   🤖 AI: Binary classification output 2️⃣ Sum of numbers 1 to n (Input 5 → Output 15)   🤖 AI: Accumulating loss values in training 3️⃣ Multiplication table of n (1 to 10)   🤖 AI: Matrix multiplication basics 4️⃣ Count digits in a number (Input 1234 → Output 4)   🤖 AI: Feature length validation 5️⃣ Reverse a number 🔴 Boss Level   (Input 123 → Output 321)   🤖 AI: Sequence reversal in NLP pipelines Try all 5 → drop your score below: 1/5 🌱 Beginner · 3/5 💪 Intermediate · 5/5 🔥 Python Pro 💡 Bonus Tips: → Break the problem into steps before coding → Use #while for digit-based problems → Use #for for counting problems → Never forget — #input() always returns a string! --- 💬 Drop your score in the comments 👇 Stuck on one? Ask — I'll help! 🤝 💾 Save · ♻️ Repost — share with someone learning Python! #60DayChallenge #Python #PythonPractice #LearnPython #PythonForAI #MachineLearning #CodingChallenge #100DaysOfCode #LearningInPublic #BuildInPublic #DataScience #CodeNewbie

Everyone asks: “Which language is AI using the most — Python, Java, or something else?” Here’s the real picture 👇 🔹 Python dominates AI Not because it’s the fastest — but because it’s the easiest and has the richest ecosystem. Libraries like TensorFlow, PyTorch, and scikit-learn make building AI models much faster. 🔹 Java still matters Used in large-scale enterprise systems where performance, stability, and integration are critical. 🔹 Other languages are rising C++ → high-performance AI systems R → statistics & data science Julia → scientific computing (growing fast) JavaScript → AI in web apps 💡 The truth: AI isn’t about the language — it’s about solving problems. Python just happens to make that journey smoother. 🚀 If you're starting in AI today: Start with Python. Master the concepts. Then explore others as needed. #AI #MachineLearning #Python #Programming #TechCareers

Day 4 of my 60-Day Python + AI Roadmap. 🚀 Today I learned something that sounds simple — but is the foundation of every AI model, every ML pipeline, and every Python script ever written. 👉 Python Variables & Naming Rules. 🧠 Why variables matter in AI: Every AI program you'll ever write starts with variables. When you train a model: learning_rate = 0.001 epochs = 50 model_name = "GPT-mini" That's it. Variables. Already doing AI stuff. 😄 📌 Key things I learned today: ✅ Variables are just names pointing to stored values ✅ Python doesn't need you to declare a type (dynamic typing!) ✅ Names must start with a letter or underscore — NOT a number ✅ Names are case-sensitive → Age ≠ age ✅ Never use Python keywords like class, if, for as variable names ✅ Always use snake_case → total_price, user_age ❌ Common mistakes beginners make: 1name = "wrong"  ← starts with a number user-name = "wrong" ← hyphen not allowed class = "wrong"  ← reserved keyword 💡 Bonus Tip (Python superpower!): Variables can change type anytime — x = 10 → x = "Hello" ✅ Totally valid! This is called dynamic typing — one of Python's biggest advantages over Java or C++. 🗺️ What's coming next on the roadmap: Day 5 → Operators Day 6 → User Input Day 7 → Conditionals (if/else) ...all building toward AI & ML projects by Day 60. --- 💬 Tell me in the comments: Are you also learning Python for AI/ML? Drop a 🙋 below — let's learn together! 💾 Save this post if you're on a similar journey. ♻️ Repost to help fellow learners in your network. #60DayChallenge #Python #LearnPython #ArtificialIntelligence #MachineLearning #PythonForAI #CodingJourney #AILearning #100DaysOfCode #DataScience #PythonBeginners #LearningInPublic #TechEducation #CodeNewbie #BuildInPublic

Learning Python is overrated in 2026. What’s underrated is this: BUSINESS THINKING Most people rush to learn Python, Pandas, machine learning. But they still struggle to answer one simple question: “So what?” They can build models. They can automate pipelines. But they can’t connect their work to revenue, cost, or growth. That’s the real bottleneck. Because companies don’t pay for code. They pay for decisions. The analysts who stand out today aren’t the most technical. They’re the ones who can: - Frame the right problem - Translate data into clear insights - Recommend actions with confidence Python is still useful. But it’s just a tool. If you want to be valuable in 2026, learn how the business actually works.

https://lnkd.in/esa_jyk2 Intermediary Python with real AI/ML - Learning Roadmap By Rubem Didini Filho. Outline; 1. Introduction section with why learn Python/AI 2. Beginner's Roadmap with phases 3. Course Comparison (now in plain text instead of table) 4. Getting Started Checklist 5. OOP Practice Guide 6. Community Advice 7. Next Steps & Resources 8. FAQ 9.APPENDIX 1: Welcome to your journey into Python and AI programming! Python is the 1 language for AI, machine learning, and data science. It's beginner-friendly, in high demand across industries, and incredibly versatile. Whether you want to build AI models, automate tasks, or create web applications, Python gives you the tools to make it happen. AI is transforming how we live and work, and learning to use Python for AI will open doors to exciting careers and creative projects. This guide introduces you to five top-tier courses, outlines a clear learning path, and shares practical advice from the global developer community. By the end, you'll know how to choose the right course, what to expect, and how to build a portfolio of AI-powered projects. Why Python for AI? Python is the main language for machine learning and AI development. It's readable, has a massive ecosystem of libraries, and is supported by a vibrant community. .. Sponsor by BUILDUP-AI.COM.BR

🐍 I began learning Python today. Here is precisely how my day was spent. No fluff. No shortcuts. Just the basics. Today I covered Python syntax, variables, data types, operators, input/output, conditionals, and loops — then put it all to work by solving around 20 beginner problems including factorial, bubble sort, palindrome check, and leap year validation. The goal was never just to get the right output. It was to understand why every line works. This is Day 1 of a long, structured path: Python → Django → ML → Deep Learning → NLP → MLOps Full progress documented on GitHub: https://lnkd.in/gGsjWtee Progress, not perfection. 🚀 #Python #MachineLearning #Django #LearningInPublic #100DaysOfCode #DataScience

Since my college days and throughout most of my career, I’ve primarily worked with Python. It’s expressive, and has become the default language for AI and Machine Learning. But Python does have some drawbacks. One of the biggest ones is performance. Even though we have modules for multiprocessing and threading, at the core Python still struggles with things like multithreading because of the Global Interpreter Lock. Compared to systems languages like C++, it can be slower. This is also why many AI frameworks implement their performance-critical parts in C/C++ under the hood. This challenge is often referred to as the “two-language problem” in AI development: - Prototype in Python - Rewrite performance-heavy components in a faster language Recently I started looking into 𝐌𝐨𝐣𝐨, 𝐚 𝐧𝐞𝐰 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 designed specifically for AI and high-performance workloads. The idea behind Mojo is quite interesting: • Python-like syntax and developer experience • Performance closer to systems languages • Designed for modern AI and hardware acceleration So in theory, it aims to combine Python’s usability with C++-level performance. From the small examples I’ve tried so far, the syntax feels very familiar to Python, but with better performance. Still early for me — I’ve only experimented with it a bit and plan to explore more. Curious to know: Has anyone here worked with Mojo yet? Would love to hear your thoughts or experiences. #AI #MachineLearning #Python #Mojo #Programming