Overcoming Python's Performance Drawbacks with Mojo

🚀 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

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

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

🚀 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

Started a week-long intensive Python course at Nuffield College, University of Oxford today. Python for Health, Economic and Social Data Science. The afternoon research talk that followed the morning lecture covering object types, collections and the building blocks of the language was very intriguing. Rather than presenting a real project, Charles Rahal prompted ChatGPT Codex and we all watched it fabricate its own data, write the code, run the analysis and produce something close to a publishable scientific paper within minutes, from a single prompt. LLMs doing serious, tedious, important research work at that kind of speed is genuinely something to pay attention to. But the takeaway from today was less about the speed and more about what you need to bring to it. Without understanding the basics yourself, you can't tell what's right from what's plausible-looking but wrong.

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 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

🚀 Day 3 of my AI Learning Journey. Today, I explored one of the most important foundations in Python — Data Structures. ⏱️ What I explored today: 🔹 Lists – storing and modifying collections of data 🔹 Tuples – immutable data structures 🔹 Dictionaries – storing data using key-value pairs 💡 Why this matters: Data structures are the backbone of problem-solving in programming. In AI and Machine Learning, data is everything — and understanding how to store and manage it efficiently is a crucial skill. 💡 Impact of learning: ✔ I now understand how to organize and access data effectively ✔ Learned when to use lists vs tuples vs dictionaries ✔ Improved my thinking in terms of structured data handling ✔ Gained confidence in writing cleaner and more logical code 🎯 Next step: Applying these concepts by building small Python projects and moving towards problem-solving. Consistency is the goal — one step at a time 🚀 #Python #DataStructures #AIJourney #MachineLearning #LearningInPublic #StudentDeveloper #Coding

I used to think I understood Python and ML… until I actually tried to explain why things work the way they do. Here are a few tools that genuinely changed how I understand things: • Python Tutor: Step through your code line-by-line and see how variables move in memory. If you struggle with recursion, pointers, or debugging. • Immersive Math: Linear algebra and calculus finally start making sense visually. Most people skip this foundation and it shows later. • bbycroft LLM Visualizer: One of the clearest ways to understand how transformers actually work. Tokens, attention, flow. • ML Visualizer: Helps you see decision boundaries and how models learn over time. Great for building real ML intuition. • Ostralayan: Explains modern AI ideas in a much more intuitive way than most papers. Good for connecting theory → practical understanding. Most people learn by consuming content. Very few learn by building intuition. That’s the difference between knowing… and actually understanding.

🚀 Python + AI: One of the Most Powerful Tech Combinations in 2026 Python continues to dominate the tech industry, especially in Artificial Intelligence and Machine Learning. Today, many organizations are building AI-powered applications using Python frameworks and libraries. 🔹 Why Python is leading in AI development? • Simple and readable syntax • Huge ecosystem of libraries • Strong community support • Powerful frameworks like TensorFlow, PyTorch, and LangChain From chatbots to recommendation systems and predictive analytics, Python is driving innovation across industries. 💡 Key takeaway: Learning Python today not only opens doors in software development but also in AI, data science, and automation. #Python #ArtificialIntelligence #MachineLearning #TechTrends #Programming