Matrix Multiplication in Python

🚀 Generators: Memory-Efficient Iteration (Python) Generators are a special type of function that allows you to create iterators in a memory-efficient way. Instead of returning a list of values, generators yield values one at a time using the `yield` keyword. This is particularly useful when dealing with large datasets, as it avoids loading the entire dataset into memory. Generators can be implemented using either generator functions (using `yield`) or generator expressions (similar to list comprehensions but with parentheses). Generators are essential for optimizing memory usage and improving performance in data processing applications. #Python #PythonDev #DataScience #WebDev #professional #career #development

The Real Story Behind Mega-Net BizLens I’ve been analyzing data for years using Machine Learning with Python. Along the way, I realized something: the process was often complex, repetitive, and time-consuming. So I asked a simple question— “What if this could be easier?" That question led me to build Mega-Net BizLens—a tool designed to simplify data analysis and turn it into clear, actionable insights.

Built a Python Event Scheduler using: • Heap — next event retrieval • Hash Table — fast lookup • Ordered structure — range queries This project applied Heaps, Hash Tables, and Balanced Trees to support adding, canceling, updating priorities, and querying events efficiently. Great hands-on practice connecting data structures to a real scheduling problem 🚀 #Python #DataStructures #Algorithms #ComputerScience #CSUF

I implemented the Support Vector Machine (SVM) algorithm using Python and Scikit-learn to perform classification on a non-linear dataset (make_circles) Trained SVM models using Linear, RBF, and Polynomial kernels Compared model performance using accuracy score Visualized decision boundaries in 2D and 3D plots Demonstrated the kernel trick for handling non-linear data

Python Clarity Series – Episode 23 Topic: Floating Point Precision Issue 🤯 Why does this happen? print(0.1 + 0.2) Output: 0.30000000000000004 ❗ 👉 This is NOT a Python bug. It’s due to how floating-point numbers are stored in binary. 💡 Fix (when needed): round(0.1 + 0.2, 1) Output: 0.3 💡 Concept: Computers approximate decimal values internally. Important in: ✔ Financial calculations ✔ Data Science Don’t ignore this. #PythonConcepts #FloatingPoint #RealWorldCoding #python #clarity

Day 37 at Luminar Technolab Started with Pandas working with Series and DataFrames. Learned how to create, access, and manipulate data, along with basic indexing using iloc. Stepping into data analysis and EDA. #Python #Pandas #DataAnalysis #LearningJourney #Consistency

Task 2: Exploratory Data Analysis (EDA) Description: Perform an exploratory analysis on a given dataset to identify patterns, trends, and summary statistics. Tools: Python, pandas, matplotlib, seaborn. EDA is one of the first and most important steps in data analysis where you explore, understand, and summarize your dataset before building models or making conclusions. I will attach a video description showing how i performed this analysis using the required tools. #CodvedaAchievements #CodvedaProjects #CodvedaJourney #CodvedaExperience #FutureWithCodveda Codveda Technologies

One thing that completely changed how I think about data 👇 👉 Writing code vs Designing for scale In Python: You solve problems on a single machine In Spark: You solve problems across a cluster of machines Same problem. Totally different thinking. Example: - Python → Loop through list and calculate sum - Spark → Use distributed transformations like "map" and "reduce" The real shift is: ❌ “How do I write this function?” ✅ “How will this run across multiple nodes efficiently?” This is where many developers struggle when moving to Big Data. It’s not about syntax. It’s about distributed thinking. Learning this the hard way, but enjoying the process 🚀 #DataEngineering #BigData #Spark #LearningInPublic

I started solving algorithmic problems daily to improve how I think, not just how I code. So I built this repository: 22+ Python problems → from basic to challenging Focused on logic, patterns, and problem-solving Some examples: Array manipulation String processing Mathematical logic Pattern-based problems This is less about “solutions” and more about building thinking frameworks. 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 𝗼𝗿 𝗽𝗿𝗲𝗽𝗮𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀, 𝘁𝗵𝗶𝘀 𝗺𝗶𝗴𝗵𝘁 𝗵𝗲𝗹𝗽. I’m consistently adding more problems and solutions as part of my daily practice. If you want to follow along or use it as a resource, the repo is in the comments. #Python #Algorithms #Coding #DataStructures #Learning

The #ArcGIS API for Python 2.4.3 is here. Unlock new automation with easier dashboard cloning, AI-powered text and image analysis, spectral workflows, anomaly detection, and more. https://ow.ly/neGq50YIMIc