Python Basics for Machine Learning Engineers

I understood NumPy better when I applied it to real data 👇 Learning concepts is one thing… But using them on actual data is different. So I tried a simple example: 👉 Dataset: list of student marks Task: Add 5 bonus marks to every student Using Python list: - needed a loop - more lines of code Using NumPy: - converted list → array - added 5 in a single step That’s it. What I realized: NumPy is not just about syntax. It’s about handling data efficiently at scale. Even a small example made it clear: - less code - faster execution - cleaner logic Now I’m focusing more on applying concepts, not just learning them. If you're learning NumPy, try this: 👉 Take any small dataset and apply operations on it That’s where real understanding begins. What’s one concept you learned but haven’t applied yet? #NumPy #Python #DataScience #DataEngineering #MachineLearning #CodingJourney #TechLearning

I decided to go all in on Python for data engineering. 🐍 Here's everything I've learned in just the first week: → Data types, variables & expressions → Lists, tuples, sets, and dictionaries → Conditionals, branching, and loops And in the coming week, I'll be starting the fun part — functions, classes, pandas, NumPy, and working with APIs. I used to think coding was for "technical" people. Turns out it's just logic + practice. What's one Python concept you wish you'd learned sooner? Drop it below — I'm taking notes. 👇 #Python #DataEngineering #LearningInPublic #TechCareer

Learning SQL or Python can feel overwhelming at first — and that’s okay. Every expert you admire once struggled with basic syntax, errors, and self-doubt. Progress in tech is not about learning everything in one day; it’s about showing up consistently, even on the hard days. A small query written today, a bug fixed tomorrow, a concept understood next week — all of it counts. Don’t compare your chapter 1 to someone else’s chapter 10. Keep practicing, keep asking questions, and keep building. Growth is often quiet before it becomes visible. Stay patient with yourself — your effort will pay off. #SQL #Python #LearningJourney #Consistency #CareerGrowth #DataAnalytics

🚀 Learning Something New Every Day! Today I learned an important Python concept — *args and **kwargs. 🔹 *args allows a function to take multiple positional arguments (stored as a tuple) 🔹 **kwargs allows a function to take multiple keyword arguments (stored as a dictionary) 💡 This makes functions more flexible and reusable in real-world scenarios. Here’s a simple example: def demo(*args, **kwargs): print(args) print(kwargs) Step by step, I’m strengthening my Python fundamentals and building a strong base for data analytics. #Python #LearningJourney #Coding #DataAnalytics

Real world data is messy. So I built my own 100-record JSON dataset to practice cleaning it with Python. The dataset included: • Duplicate entries • Missing values • Ratings in mixed formats like "five", "4", and "3.5" • Different types of customer feedback • Inconsistent formatting Using Python, I cleaned the data, removed duplicates, standardised ratings, and generated basic insights. Big takeaway: data cleaning is just as important as analysis. GitHub Repo: https://lnkd.in/gYr-4kkm #Python #DataScience #DataAnalytics #Projects #Coding

Day 5 Consistency is key! 🚀 I’ve been dedicating time to strengthening my Python fundamentals, specifically diving deep into how to work with data sequences. From understanding immutability to mastering indexing and slicing techniques, I’m building a solid foundation to handle data manipulation more effectively. It’s rewarding to see how these concepts translate into cleaner, more efficient. Today I’ve been practicing advanced sequence manipulation in Python. Key takeaways from my study session: Immutability: Understanding why certain data types (like strings) cannot be changed in place. Slicing Syntax: Mastering [start:stop] and how to omit indices for cleaner, faster code. Negative Indexing: Leveraging indexing from the end to make my code more dynamic. There is always something new to learn when it comes to optimizing data extraction! 💡 #PythonProgramming #SoftwareDevelopment #LearningToCode #DataManipulation #CodingTips #Python #CodingJourney #ContinuousLearning #DataHandling #SelfDevelopment #TechSkills

🚀 Python Practice – Function Examples Taking my Python learning a step further by practicing real-world function-based problems 🐍 In this session, I worked on: ✔️ Temperature Conversion (Celsius ↔ Fahrenheit) ✔️ Password Strength Checker ✔️ Shopping Cart Total Cost Calculator ✔️ Palindrome Checker ✔️ Factorial using Recursion These examples helped me understand how functions can be used to solve practical problems and write reusable, structured code. A big thanks to Krish Naik Sir for his amazing teaching and clear explanations 🙌 Documented all my practice in a Jupyter Notebook and shared it as a PDF to track my progress. Learning by building real logic step by step 📊 #Python #Functions #Practice #LearningJourney #DataAnalytics #Coding

Recursion confused me. So I built a visualizer for it. 🐍 Most beginners (including me) struggle with one thing: "What actually happens when a function calls itself?" So I wrote a Python program that shows you — step by step, with a delay so you can actually follow it. Watch it go DOWN the stack, hit the base case, then come back UP — adding numbers on the way. 👇 What I used: → Recursion — function calling itself → time.sleep() — to slow execution down visually → Print statements — to trace every step This isn't from a tutorial. I built this because I was confused. That's the best reason to build anything. 💡 Important: Dry run your code, It helps alot. #Python #Recursion #LearningInPublic #DataAnalytics #BBA #BuildInPublic

Ever had a Python variable that should work… but suddenly doesn’t? No error. No warning. Just confusing behavior. That’s usually not a logic problem — it’s a scope problem. In Python, variables don’t exist everywhere. They live inside specific boundaries, and Python follows a strict search order to find them. Miss that… and your code starts behaving in ways that feel completely unpredictable. In my latest article, I simplified this concept into a clear mental model: • Why variables “disappear” inside functions • How Python decides which value to use • The real reason behind those “it worked before” bugs • A simple way to think about scope without memorizing rules If you’re working with Python — whether for data analysis, ML, or backend — this is one of those concepts that quietly affects everything. I’ll drop the link in the first comment 👇 What confused you more when learning Python: scope or debugging unexpected behavior? #Python #Programming #DataScience #Coding #Debugging #TechLearning

Writing clean, predictable code is just as important as the analysis itself. In Python, understanding memory references is the "hidden" skill that separates scripts that work from scripts that scale. I see many developers struggle with unexpected mutations when handling nested data structures. A simple new_list = old_list doesn't just copy the data; it copies the problem. I just published a deep dive into "Why Your Python List Copies Keep Betraying You." It’s a guide to mastering the copy module so you can stop debugging "impossible" errors and start building more resilient data pipelines. #PythonProgramming #DataAnalytics #TechWriting #CleanCode #MachineLearnin