Er. Malathi Sreekumar’s Post

Day 14 of my AI & Data Science Journey Today, I learned about functions in Python, with a focus on implementing nested functions. What I explored: Concept and components of functions Types and classification of functions Implementation of user-defined functions Key focus: Nested functions (function inside another function) How inner functions can access variables from the outer function Practical implementation of nested functions to organize code better Practiced writing programs using nested functions to break down problems into smaller parts. ✨ Key Insight: Nested functions help improve code structure, readability, and reusability by organizing logic within a function. They are useful when a function is needed only within another function. #Python #Programming #AI #DataScience #LearningJourney #Coding #Functions #Consistency

Python looks simple on the surface… but the real power runs deeper. Clean syntax outside. Powerful engine inside. ⚡ That’s why tools like NumPy, Pandas, and even AI libraries feel so powerful. Sometimes, the beauty you see is powered by something even stronger underneath. #Python #Programming #AI #cpython #MachineLearning #DataScience #Coding

Today, I started diving into the basics of Python, the programming language at the heart of AI and Machine Learning. I explored different data types like integers, floats, booleans, complex numbers, and strings, and learned the rules for using parentheses and other syntax essentials. My Key Takeaways: Choosing the right data type is critical for correct operations Understanding Python syntax ensures your code runs smoothly These foundational concepts make everything else in AI/ML easier to learn Python may seem simple at first glance, but mastering the basics is the first step to building complex AI solutions. #Python #AI #MachineLearning #DataScience #30DayChallenge #M4ACE

🚀 Just delved into a fascinating exploration of random number generation and various distributions in Python, using numpy and matplotlib. From understanding uniform distributions and normal distributions to simulating coin flips and drawing from discrete sets, it's incredible how powerful these tools are for statistical analysis and modeling. Learning to seed the RNG for reproducible results, visualizing CDFs, and even creating random DNA sequences! This foundational knowledge is crucial for everything from A/B testing to machine learning. What are your favorite random number generation tricks or applications? DataScience #Python #Numpy #Matplotlib #Statistics #RandomNumbers #MachineLearning #DataAnalysis #Coding

✓ Advance Python Course with Machine and Deep Learning. ✓ Exercise ( Task 21 ). ✓ Statement:- 1) ----- Write a program that demonstrates a system error and then prints its output. #LearningInPublic #CodingNewBie #PythonCourse #Programming #FutureGoals #Coding

Completed learning Regularization in Machine Learning ✅ Understood how: 👉 Overfitting affects model performance 👉 Regularize High coefficient to Low coefficient l2- Regression| l2 Regularization 👉 Regularize High coefficient to zero - l1 👉 Lasso (L1) helps in feature selection 👉 Ridge (L2) helps in reducing model complexity Practiced implementing these concepts using Python. Step by step improving my ML skills 💻📈 #MachineLearning #Python #DataScience

✓ Advance Python Course with Machine and Deep Learning. ✓ Exercise ( Task 16 ). ✓ Statement:- 1) ----- Use the global keyword to modify a global variable value in python. #LearningInPublic #CodingNewBie #PythonCourse #Programming #FutureGoals #Coding

Python Basics for Machine Learning I’ve uploaded a video covering the core Python data structures used in machine learning: • Lists • Tuples • Sets • Dictionaries These concepts are essential for handling data and writing efficient ML code. This video is part of my Advanced Machine Learning with LLM series, focused on building strong foundations before moving into complex topics. https://lnkd.in/gSg6rBKM #Python #MachineLearning #DataStructures #LLM #AI #Learning

✓ Advance Python Course with Machine and Deep Learning. ✓ Exercise ( Task 11 ). ✓ Statement:- 1) ----- Define a global variable (global_var) and prints it's value in python program. #LearningInPublic #CodingNewBie #PythonCourse #Programming #FutureGoals #Coding

I’ve recently explored RapidFuzz, a powerful Python library used for fast and efficient string matching and similarity scoring. Through this learning, I understood how fuzzy matching can help in real-world data problems where exact matches are not always possible. It’s impressive how quickly it can compare large sets of text and find the closest matches with high performance. This small step has really improved my understanding of how data cleaning and matching works behind the scenes in real applications. Still learning and improving step by step — more updates coming soon! 💻✨ #Python #DataScience #MachineLearning #LearningJourney #FuzzyMatching #RapidFuzz #AI