Python for AI/ML: How Much Do You Need to Know?

https://lnkd.in/eHjVY9tJ 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.... Sponsor by BUILDUP-AI.COM.BR

This changed how I think about Python. I recently completed The Complete Python Bootcamp: From Zero to Hero. Before this, Python felt like just a programming language. Now, I see it as the foundation behind everything I want to build in AI. From: • Writing clean logic • Handling real-world data • Automating repetitive tasks To now being able to: 👉 Build projects like my AI Legal Assistant (CLiC) This course helped me connect the dots. Not just “how to code” — but how to use code to solve problems. I already had experience using Python to build AI tools, but this helped me strengthen my fundamentals and level up my skills. Next focus: applying this more in real-world projects and data workflows. What’s one skill you think is underrated but essential in AI? #Python #AI #MachineLearning #Programming #LearningInPublic #BuildInPublic #TechCareers

https://lnkd.in/esa_jyk2 Sponsor by BUILDUP-AI.COM.BR 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...

🚀 New Video: Ridge vs Lasso vs Elastic Net in Python | Regularization Techniques Explained In machine learning, building models that generalize well to unseen data is critical. One of the most powerful ways to control model complexity and prevent overfitting is Regularization. In my latest YouTube video, I explain and demonstrate three widely used regularization techniques: 🔹 Ridge Regression (L2 Regularization) – Shrinks coefficients to reduce model variance and handle multicollinearity. 🔹 Lasso Regression (L1 Regularization) – Performs automatic feature selection by forcing some coefficients to become zero. 🔹 Elastic Net Regression – Combines L1 + L2 penalties, balancing feature selection and coefficient shrinkage for better performance when predictors are correlated. 📊 In this tutorial, you will learn: ✔ The intuition behind regularization ✔ Mathematical differences between Ridge, Lasso, and Elastic Net ✔ When to use each technique ✔ Hands-on implementation using Python and Scikit-Learn 🎥 Watch the full video here: https://lnkd.in/dRriSAj9 This video is especially useful for students, data analysts, and machine learning practitioners who want to strengthen their understanding of regression modeling and feature selection. #MachineLearning #DataScience #Python #ScikitLearn #Regression #Regularization #RidgeRegression #Lasso #ElasticNet #AI #DataAnalytics

One small Python concept today… but a very important one. Today’s lesson focused on how Python handles mutable objects like lists. In the example below: def add_item(lst): lst.append(100) a = [1, 2, 3] add_item(a) print(a) The result will be: [1, 2, 3, 100] Why? Because lists in Python are mutable. When we pass a list to a function and modify it using methods like append(), the change happens in-place — meaning the original list itself is modified. 💡 Key takeaway: Understanding the difference between mutable and immutable objects is essential for writing predictable and efficient Python code. Every day in this sprint reminds me that small concepts build strong foundations in data analytics and AI. On to the next challenge. 🚀 #Python #DataAnalytics #AI #MachineLearning #LearningJourney #Coding #TechSkills #AIAnalytics #PythonProgramming #LinkedInLearning

Most people don’t fail at learning Python… they fail at following a clear roadmap. Most people start learning Python with excitement… and quit within a few weeks. Not because Python is hard — but because there’s no clear direction. What I found interesting is this: a simple 15-day roadmap can completely change how you learn. Instead of overthinking, it focuses on execution: Day 1 → Basics & setup Day 3 → Logic building with loops Day 7 → Real data structures Day 10+ → OOP & real-world concepts Day 15 → Intro to Machine Learning No fluff. Just progress. The biggest shift? You stop “watching tutorials” and start solving problems daily. Because in tech, your growth is directly proportional to the problems you solve. If you're stuck in tutorial hell, try this: Learn less. Practice more. Repeat daily. That’s how beginners turn into developers. Connect with Himanshu Choure for more #PythonLearning #CodeNewbie #LearnProgramming #DevelopersLife #TechCareer #CodingMotivation #BuildInPublic #AI #MachineLearning #SkillBasedLearning

Mastering Python Algorithms: Turning Logic into Power In the world of programming, syntax gets you started… but algorithms make you unstoppable. Over the past few weeks, I’ve been diving deep into Python algorithms — not just solving problems, but understanding the why behind every solution. And here’s what I’ve realized 👇 💡 Algorithms are not just code — they are thinking patterns. From simple sorting techniques to complex problem-solving strategies, each algorithm teaches you how to: Break problems into smaller pieces 🧩 Optimize performance ⚡ Think logically under pressure 🧠 ✨ What I explored: ✔️ Sorting algorithms (Quick Sort, Merge Sort) ✔️ Searching techniques (Binary Search) ✔️ Recursion & Backtracking ✔️ Time & Space Complexity (Big-O) 🔥 The biggest lesson? It’s not about memorizing solutions — it’s about building the ability to think like a problem solver. Every bug, every failed attempt, every “why is this not working?” moment is actually shaping your mindset into something powerful. 📈 Consistency > Perfection Even 1 problem a day can transform your thinking over time. If you're learning Python, don’t just code — train your brain. #Python #Algorithms #CodingJourney #ProblemSolving #100DaysOfCode #SoftwareEngineering #Learning #Tech

🚀 Day 6 of My AI/ML Learning Journey | Diving Deeper into Python 🐍 Every day of learning Python is unlocking a new layer of understanding. Today’s focus was on File Handling, Exception Handling, and efficient Python techniques that make programs more robust and practical. 💻✨ 📚 Topics Covered Today: 📂 File I/O in Python 🛠 Operations on Files 🔑 File Modes (read, write, append, etc.) 🤝 Using the with keyword for safer file handling 🗑 Deleting Files 🧩 Practice Problems ⚠️ Exception Handling 🔚 finally Keyword ⚡ List Comprehensions 📄 Working with JSON Module 💡 Key Takeaway: Understanding file handling and exception handling makes programs more reliable and production-ready, while techniques like list comprehensions help write clean and efficient code. Small progress every day → Big transformation over time. 🚀 Still going strong on my #100DaysOfCode journey. #AI #MachineLearning #Python #CodingJourney #100DaysOfCode #LearningInPublic #BuildInPublic #Consistency

🐍 Python isn’t hard — lack of direction is. Over the past few weeks, I focused on learning Python the right way: Basics → Logic → OOP → Real Projects → AI/ML Once I followed a roadmap, everything started making sense. No confusion. Just clarity. If you're learning Python, don’t just learn — follow a path. Build. Repeat. Improve. 🚀 #Python #Coding #MachineLearning #AI #Learning #Tech