Bikki Singh’s Post

Python Learning Progress | 29th Jan Today’s Python class was focused on Regular Expressions (Regex) — a really powerful module used for pattern matching and text manipulation. We practiced concepts like: ✅ compile() ✅ search() ✅ findall() ✅ split() ✅ sub() It was interesting to see how efficiently Python can handle text processing with Regex. Looking forward to practicing more and strengthening these skills step by step. 🚀 #Python #Regex #Learning #CodingJourney #Programming Pooja Chinthakayala

📊 Exploring Regression Analysis with Python Recently, while studying Regression Analysis, I worked on building regression models using the Statsmodels library in Python and explored how statistical summaries help in understanding a model beyond just predictions. Instead of only fitting a model, I focused on interpreting the regression summary output to understand the story behind the data. The summary provides several statistical insights such as: 🔹 R-squared & Adjusted R-squared – Measuring how well the model explains the variability in data 🔹 p-values – Identifying which variables are statistically significant 🔹 t-tests – Checking the significance of individual coefficients 🔹 F-statistic – Evaluating overall model significance 🔹 Confidence Intervals – Understanding the reliability of coefficient estimates 🔹 Residual diagnostics – Assessing model assumptions This approach helps move from just building models ➝ to making statistical inferences and reliable predictions. One thing I realized is that understanding these statistical indicators is crucial for interpreting models correctly, especially when working with real-world datasets. #DataScience #MachineLearning #RegressionAnalysis #Python #Statsmodels #DataAnalytics #LearningJourney #AndrewNG

Python Sets — Why They’re More Useful Than You Think In simple words: A set in Python is a collection that: • Stores only unique values. • Doesn’t maintain order. • Allows fast membership checks. Why it matters: - Removing duplicates becomes easy. - 'in' operations are much faster than lists. - Set operations like union & intersection are powerful in real-world logic. If you're serious about writing cleaner Python code, sets are essential. Which set operation do you use most in real projects? #Python #LearnPython #DataStructures #BackendDevelopment #PythonDeveloper #Coding

📌 How Does Python Store Variables in Memory? Today I searched about how Python stores variables in memory, and here’s what I understood 👇 In Python, variables are references to objects, not containers that directly hold values. When we write : x = 10 Python does NOT store 10 inside x. Instead: 1️⃣ Python creates an object in memory for the value 10. 2️⃣ Then it makes the variable x point (reference) to that object. So basically: Variables in Python store memory addresses (references), not raw values. 🧠 What happens with multiple variables? a = 10 b = 10 Both a and b may point to the same object in memory (especially for small integers), because Python optimizes memory using something called interning. 🔄 What about mutable objects? For example: list1 = [1, 2, 3] list2 = list1 Now both variables reference the SAME list object. If we modify: list2.append(4) Both will change because they point to the same memory location. 💡 Key Concepts: • Everything in Python is an object. • Variables store references. • Immutable objects (int, float, string) behave differently from mutable ones (list, dict, set). • Python uses automatic memory management and garbage collection. Understanding memory behavior is essential for writing efficient and bug-free code — especially when working with large datasets or AI models. #Python #Programming #ComputerScience #LearningJourney #SoftwareEngineering

🚀 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

The power of agentsAI in performing tasks is impressive. In this example, I use natural language to generate SQL. Tools: - LLM - Python - SQL https://lnkd.in/dUUabqfu #agentAI #SQL #LLM #Python

Quick Python Challenge What will be the output of this code? a = [1, 2] b = a b.append(3) print(len(a)) Options: A) 2 B) 3 C) 1 D) Error 💡 At first glance, many people think the answer is 2. But the correct answer is actually 3. Why? Because in Python: b = a does not create a new list. It simply makes b reference the same object in memory as a. So when we run: b.append(3) we are modifying the same list that both a and b point to. The list becomes: [1, 2, 3] So: len(a) = 3 📌 Key Insight: In Python, variables can reference the same mutable object, which means modifying one reference affects the other. 🔥 Lesson of the day: Understanding mutable objects and references is essential when writing reliable Python code—especially in data analysis and AI pipelines. 💬 Curious: Did you get it right on the first try? #Python #AI #DataAnalytics #LearningInPublic #30DayChallenge #PythonTips

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

🗑Data Cleaning for Machine Learning — Python Made Simple Data cleaning is one of the most important steps in any Machine Learning workflow. Before models can learn, your data needs to be consistent, structured, and free of noise, and Python gives you all the tools to make that happen efficiently. This useful and intuitive guide walks through the essential techniques for cleaning data with Python. From handling missing values and fixing inconsistent formats to encoding categories and scaling features, helping you prepare high‑quality datasets that lead to better models and better insights. #Python #MachineLearning #DataCleaning #DataScience #Analytics