Abhishek Sharma’s Post

If you’re learning Python for AI… there’s a high chance you’ve felt this: Confused. Overwhelmed. Jumping between tutorials. But still not building anything real. That’s exactly where Dave Ebbelaar stands out. He doesn’t just teach Python. He teaches how to think like a builder in AI. No fluff. No overcomplication. Just clean, structured learning that actually helps you move forward. What I personally like about his approach: → He breaks complex concepts into simple steps → Focuses on projects, not just theory → Helps you understand the “why”, not just the “how” Because in AI and Data… Knowing syntax won’t get you paid. Building things will. If you’re a quiet learner trying to enter AI or Data, you don’t need 50 courses. You need 1–2 solid mentors and the discipline to execute. Dave can be one of them. Key Takeaway: Don’t just consume content. Follow people who help you build clarity + capability. Have you come across someone who genuinely simplified AI or Python for you? Drop their name below 👇 Let’s help each other learn smarter.

🚀 Master Machine Learning in Python – From Basics to Advanced Concepts Just explored an amazing set of course notes on Machine Learning in Python, and here are some key takeaways that every aspiring data scientist should know 👇 📌 1. Linear Regression – The Foundation * Understand relationships between variables * Learn concepts like R-squared, OLS, and assumptions * Build predictive models using real-world data 📌 2. Logistic Regression – Classification Made Easy * Predict probabilities instead of exact values * Learn logit functions & model accuracy * Evaluate performance using confusion matrix 📌 3. Clustering – Discover Hidden Patterns * Group data without labels (unsupervised learning) * Learn K-Means clustering & centroid concept * Use techniques like the Elbow Method to find optimal clusters 📌 4. Model Optimization Concepts * Avoid overfitting & underfitting * Use training vs testing data effectively * Understand assumptions like no multicollinearity & homoscedasticity 📌 5. Distance & Similarity Metrics * Euclidean distance for clustering * Helps in grouping similar data points efficiently 💡 One powerful insight: Machine Learning is not just about models — it’s about understanding data, assumptions, and interpretation. These notes are a solid roadmap for anyone starting their ML journey with Python. Pdf credit goes to respective owner. If you want to go beyond theory and build production AI systems, I recently launched a course where we build: * ReAct Agents * Agentic RAG systems * Multi-Agent workflows * Memory-enabled AI agents * Human-in-the-loop applications using LangGraph, one of the most powerful frameworks for building AI agents. 🎓 Course Link https://lnkd.in/dAbXUNwm For the first 100 learners, the course is available at a special discounted price. Follow Pratham Uday Chandratre for more!

A year ago, learning Python meant writing scripts and building APIs. Today, it feels like I’m learning how to build systems that can think. That shift is real. With Agentic AI, Python is no longer just about: • functions • classes • frameworks It’s about creating workflows where: • an agent understands a problem • decides what to do next • calls APIs or tools • adapts based on results ⸻ I recently started exploring this space, and one thing stood out: 👉 You’re not just coding anymore 👉 You’re designing behavior ⸻ There are moments where: You write a piece of code… and the system responds in a way you didn’t explicitly program. That’s powerful. And honestly, a bit uncomfortable too. ⸻ Because now the challenge is not just: “How do I build this?” It becomes: • How do I guide this system? • How do I control its decisions? • How do I trust its output? ⸻ As someone working in integration and architecture, this feels like a major shift. We’re moving from: 👉 predictable systems to 👉 adaptive systems ⸻ And Python is right at the center of this change. ⸻ Curious — Are you still learning Python the traditional way, or exploring it through AI and agentic workflows? ⸻ #AgenticAI #Python #AI #SoftwareArchitecture #TechLearning #FutureOfTech

Just Published: Mastering Python for Machine Learning: A Practical, No-Nonsense Roadmap If you're someone who feels confused about where to start in Machine Learning, this guide is for you. I’ve broken down the journey into simple, practical steps 💡 No unnecessary theory. No confusion. Just a clear roadmap you can actually follow. Whether you're a beginner or someone restarting your ML journey, this will help you build a strong, real-world foundation. 👉 Read here: https://lnkd.in/gBKzWiUK I’d love to hear your thoughts and feedback! 🙌 #Python #MachineLearning #DataScience #AI #Learning #CareerGrowth

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗔𝗜 Is learning Python "easier" in 2026? Yes. But it’s also different. 🐍✨ For a beginner like me, AI isn't just a "cheat code"—it’s a 24/7 personal tutor. Here is how AI is fundamentally changing the way we learn Python today: 🧠 𝗧𝗵𝗲 𝗦𝗼𝗰𝗿𝗮𝘁𝗶𝗰 𝗧𝘂𝘁𝗼𝗿: Instead of just giving the answer, modern AI assistants (like the latest Gemini or Socratic AI tutors) now ask: "I see a syntax error on line 5—what do you think is missing in your function call?" It forces me to think, not just copy. 🔍 𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 "𝗕𝗹𝗮𝗰𝗸 𝗕𝗼𝘅": When I hit a complex concept like 𝗗𝗲𝗰𝗼𝗿𝗮𝘁𝗼𝗿𝘀 or 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝗼𝗻, I can ask AI to "Explain this like I'm 5 years old using a LEGO analogy." Turning abstract code into relatable stories is a learning game-changer. 🛠️ 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗘𝗮𝘀𝗲: Tools like Google Antigravity or browser-based AI labs have removed the "setup headache." I can focus on logic immediately without getting stuck on path variables or environment installs. 𝗠𝘆 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿'𝘀 𝗥𝘂𝗹𝗲 𝗳𝗼𝗿 𝟮𝟬𝟮𝟲: Use AI to explain the "𝗪𝗵𝘆", but always write the "𝗛𝗼𝘄" yourself. Master the logic first, and the tools will follow. 𝗠𝘆 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆:💡 I use AI to understand the logic behind any concept of Python, and it saves me hours of confusion. Instead of just getting an answer, I get a clear explanation that helps me move forward with confidence. 𝘔𝘢𝘴𝘵𝘦𝘳 𝘵𝘩𝘦 𝘭𝘰𝘨𝘪𝘤 𝘧𝘪𝘳𝘴𝘵, 𝘢𝘯𝘥 𝘵𝘩𝘦 𝘵𝘰𝘰𝘭𝘴 𝘸𝘪𝘭𝘭 𝘧𝘰𝘭𝘭𝘰𝘸. 🚀 In the modern tech stack, Python serves as the critical engine for back-end logic, data processing, and AI integration. By mastering Python's core principles first, a developer isn't just writing scripts; they are building the architectural foundation required for the complex, intelligent systems found in a professional Web Dev Lab. The logic learned today is the infrastructure for the web applications of tomorrow. #PythonForBeginners #AIinEducation #LearningToCode #WomenInTech #Python2026 #FutureOfLearning #PythonLearning #AIinEducation #WomenInTech

Yes — Machine Learning with Python and Scikit-Learn is a strong new one, and I did not find it in your posted content history, so it’s safe to use. Your earlier posts covered beginner ML concepts and other course recommendations, but not this freeCodeCamp scikit-learn course. Memory +3 Here’s your post in the same style: 🚀 𝗧𝗵𝗲 𝗠𝗼𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗙𝗿𝗲𝗲 𝗠𝗟 𝗖𝗼𝘂𝗿𝘀𝗲 𝗬𝗼𝘂’𝗹𝗹 𝗨𝘀𝗲 𝗶𝗻 𝟮𝟬𝟮𝟲 (𝟭𝟴 𝗛𝗼𝘂𝗿𝘀 𝗼𝗳 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝗠𝗟) Nobody explains machine learning with Python and scikit-learn as clearly as this course does. I went through the full course — and it gives you the exact workflow you need to build real ML projects from scratch. If you want a practical course that goes beyond theory, this is a great free resource for 2026. 👇 📌 What You’ll Learn Inside 🔹 Linear regression + gradient descent 🔹 Logistic regression for classification 🔹 Decision trees, random forests, and gradient boosting 🔹 How to approach ML projects the right way 🔹 Unsupervised learning with scikit-learn 🔹 A full ML project from scratch 🔹 Deploying a model with Flask 📌 Full Course 🔗 is free: https://lnkd.in/dr5spnPB 💡 If you want to move from beginner ML ideas to real-world project building, this is one of the best free practical courses to study in 2026. 📚 Recommended Reading Find more world-class free AI courses 👇 🔗 Free AI Vault profile ♻️ Repost to help others discover practical ML gems 💬 𝗔𝗻𝘆 𝗼𝘁𝗵𝗲𝗿 𝗳𝗿𝗲𝗲 𝗠𝗟 𝗰𝗼𝘂𝗿𝘀𝗲 𝘆𝗼𝘂’𝗱 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱? Drop it below ⬇️ 💾 𝗦𝗮𝘃𝗲 this post — you’ll want to come back to it. 👋 Follow Free AI Vault for: → Machine Learning & Deep Learning roadmaps → 100% Free AI courses with real content → Practical projects for future AI engineers #MachineLearning #AIEngineering #FreeCourses #ScikitLearn #ML #DataScience #CareerGrowth Would you like the next one to be more deep learning, math-based, or project-based?

Part 2: My Journey Transitioning to AI Hello, I'm Kiran Gundra, sharing my journey of transitioning to AI. I have 10 years of experience in the software industry, and learning Python fundamentals was relatively easy for me. I understood the editor, syntax, and basics. Moving forward on my journey is Machine Learning (ML). I don't know what ML is or how much I need to understand it to progress. For those following my journey and looking to get started: If you have a software background like me, focus on learning Python so you can understand the code and run programs. If you have no coding knowledge, I suggest taking a beginner Python course, either online or on YouTube. I'm not providing specific links, as you can easily find great resources. The key is to get comfortable with Python programming before diving into Machine Learning. Having a strong foundation in the basics will make your ML journey much smoother. Let me know if you have any other questions as I continue sharing my AI transition experience! #MachineLearning #Python #AIJourney #CareerTransition

Everyone is learning Python. But almost no one knows how to use it with AI. That’s where the real opportunity is. Python isn’t just about syntax anymore. It’s the backbone of AI products. If you're learning Python, do this instead: → Build with AI from day one → Use tools like ChatGPT & Claude to speed up coding → Focus on solving real problems, not just tutorials Start with simple but powerful ideas: • Resume analyzer with AI feedback • Chatbot trained on your own data • Auto email writer for outreach • YouTube/blog summarizer • AI-powered finance tracker Learn APIs. That’s the real game. Python + APIs = Real-world AI apps Don’t chase perfection. Ship fast. 1 live project > 10 unfinished courses Document everything publicly. Your work will speak before you do. Right now, the edge is simple: Python + AI + Projects = Opportunities Don’t just learn Python. Build something with it. Connect Pushpendra Tripathi for more such content  Comment “Python70” and I’ll send the resource.

I started this around the same time the new semester began, where I’m taking courses in Artificial Intelligence and Machine Learning. Unsurprisingly, one topic that keeps coming up in class discussions is AI “taking over” jobs, especially roles like Data Analysts and Software Engineers. But the more we talk about it, the clearer it becomes. These jobs aren’t exactly being replaced. What is happening, though, is a shift in how the work gets done. Learning Python made that idea feel very real. Python is popular for a reason. It’s clean, readable, and surprisingly friendly. Sometimes it feels like the language itself is trying to help you understand what you’re doing. Totally a different experience when we learned Java and C++ in the Uni. 😂 Another realization during this journey: in an age where AI can generate code in seconds, the real advantage isn’t just writing code, it is actually understanding what the code is doing. Knowing how and why things work still matters. AI might give you the answer, but someone still needs to know whether that answer actually makes sense… or if it’s confidently wrong. 😅 Otherwise, you’re just copying and pasting smarter mistakes. So yes, I can now write in Python code, but more importantly, I can (usually) understand it too! 😅 Grateful for the learning experience INCO Academy, and Thalia Zamora Gomez, and excited to keep building my data skills one line of code at a time.

🚀 Quick Reminder: Python Strings & Methods Today I revised one of the most important basics in Python — Strings & their Methods 🐍 🔹 A string is simply a sequence of characters inside quotes. Example: "Hello World" 💡 Must-Know String Methods: ✅ Case Conversion upper(), lower(), title() ✅ Searching find(), index(), count() ✅ Modify replace() ✅ Remove Spaces strip(), lstrip(), rstrip() ✅ Join & Split split(), " ".join() ✅ Check Methods isalpha(), isdigit(), isalnum() ✅ Other Useful Ones startswith(), endswith(), len() 🧠 Mini Practice: Count vowels Check palindrome Remove duplicates Find character frequency ⚡ Quick Tip: Strings are immutable, which means they cannot be changed directly. 📌 Mastering strings is very important for data cleaning, NLP, and AI projects. #Python #Coding #LearningJourney #100DaysOfCode #AI #Programming #Students #PythonBasics