Reverse a List with Python Slicing

Vector databases are quickly becoming the backbone of modern AI systems — from semantic search to production-grade RAG pipelines. In this Medium article, I break down how FAISS, Chroma, and Pinecone work in Python, where each one fits best, and how to choose the right tool for real-world AI applications. If you’re building LLM-powered products or scalable search systems, this is a practical, developer-focused read worth your time. #VectorDatabases #Python #ArtificialIntelligence #MachineLearning #LLM #RAG #SemanticSearch #FAISS #ChromaDB #Pinecone #AIEngineering

Python isn’t magical. It’s precise. And precision has consequences. Continuing my deep dive into sequences… * with lists can be a little mischievous. grid = [['_'] * 3] * 3 Looks like a clean 3×3 grid. Until you do: grid[1][2] = 'X' And suddenly every row changes. Why? Because * didn’t copy the inner list — it copied the reference to it. The safer version? grid = [['_'] * 3 for _ in range(3)] Now each row is independent. Also interesting: += on lists mutates in place. * with tuples creates a new tuple object with a separate id. And when assigning to a slice, the right side must be iterable: l[2:5] = [100] The more I explore sequences, the clearer it becomes — Still learning... #PythonLearning #BackendEngineering  #FinTechCareers

🚀 Day-37 of #100DaysOfCode 🐍 Python Sorting Algorithm Challenge Today I implemented Quick Sort, a powerful and efficient sorting algorithm based on the divide and conquer technique. 🔹 What is Quick Sort? Quick Sort works by: Selecting a pivot element Partitioning the array so that elements smaller than the pivot are on the left and larger ones on the right Recursively applying the same logic to subarrays 🔹 Concepts Practiced: ✔ Recursion ✔ Partitioning logic ✔ In-place swapping ✔ Divide and Conquer strategy 🔹 Approach: Choose the last element as the pivot Rearrange elements around the pivot Recursively sort the left and right partitions 🔹 Key Insight: Quick Sort has an average time complexity of O(n log n) and is widely used due to its speed and in-place sorting nature. Implementing such algorithms helps deepen understanding of efficient data processing and algorithmic thinking 💡 #Python #QuickSort #SortingAlgorithms #DivideAndConquer #CorePython #DSA #100DaysOfCode #Day37 #LearnPython #CodingPractice #PythonDeveloper

Let’s keep it simple (and a little fun today 😄) Which of the following is a valid list comprehension in Python? A) [x for x in range(5) if x % 2 == 0] B) for x in range(5): if x % 2 == 0 C) x = [range(5) if x % 2 == 0] D) list(x for x in range(5) if x % 2 == 0) 🧠 The correct answer is: 👉 A [x for x in range(5) if x % 2 == 0] This is the classic Pythonic structure: [expression for item in iterable if condition] ✨ Option D technically works and returns a list, but A is the clean, direct list comprehension syntax. Small concept. Big readability difference. Clean code isn’t about writing more… It’s about writing smarter 🔥 #Python #AI #LearningInPublic #30DayChallenge #DataAnalytics #CleanCode

I used a simple Python chart today and it reminded me why accuracy can be misleading in machine learning. When a dataset is imbalanced (one class appears way more than the other), a model can look “good” just by predicting the majority class most of the time. Here’s what I did : 1. Plotted the class distribution 2. Checked what a “dumb baseline” accuracy would be if I always predicted the majority class 3. Decided to focus more on Precision, Recall, F1, and ROC-AUC instead of accuracy alone If 90% of the data is one class, a model can get ~90% accuracy while being useless for the minority class (which is often the important one). So, what I've learned is Before training any model, I now always do: Class distribution plot Baseline check Choose metrics that match the real goal ❓ Quick question In a high-stakes problem (fraud, health, risk), would you prioritise precision or recall — and why? #DataScience #MachineLearning #Python #DataVisualization #BuildInPublic

#Learning #Python through Chunks. Lets start journey together (Beginner to Master). Lets code together !! #ABCC - Any Body Can CODE Chunk 7: Operators (Math & Basic Actions in Python) Operators let you do things with your variables. We’ll start with the simplest category: arithmetic operators. Let’s keep this ultra‑simple. 🔢 1. Arithmetic Operators These are basic math symbols: +Addition -Subtraction *Multiplication /Division //Floor division %Remainder (mod) **Power/exponent To remember: 7 / 2   # 3.5 (normal division) 7 // 2  # 3   (floor division, drops decimals) 10 % 2  # 0 → even 11 % 2  # 1 → odd 2 ** 5  # 32 3 ** 3  # 27 CODE - Try them x = 10 y = 3 print(x + y)    # 13 print(x - y)    # 7 print(x * y)    # 30 print(x / y)    # 3.3333 print(x // y)   # 3 print(x % y)    # 1 print(x ** y)   # 1000 💡 Key things to remember / always gives float // gives integer (drops decimals) % gives remainder ** means power

Ever needed to track file changes in real-time? With 𝘄𝗮𝘁𝗰𝗵𝗱𝗼𝗴, you can do that in Python easily. 𝘄𝗮𝘁𝗰𝗵𝗱𝗼𝗴 allows you to detect file creations, modifications, and deletions instantly. See below for a small example. -> 𝗙𝗶𝗹𝗲𝗦𝘆𝘀𝘁𝗲𝗺𝗘𝘃𝗲𝗻𝘁𝗛𝗮𝗻𝗱𝗹𝗲𝗿: Handles file system events like when a file is modified or deleted. -> 𝗢𝗯𝘀𝗲𝗿𝘃𝗲𝗿: Watches the directory for changes Check it out: github(dot)com/gorakhargosh/watchdog --- ♻️ Found this useful? Share it with another builder. ➕ For daily practical AI and Python posts, follow Banias Baabe.

As an AI Engineer, it‘s important to learn design patterns to write cleaner code. I can recommend the website 𝐑𝐞𝐟𝐚𝐜𝐭𝐨𝐫𝐢𝐧𝐠 𝐆𝐮𝐫𝐮 where important design patterns are explained in a simple way + Code examples in Python. Check it out: https://lnkd.in/dnB5urTC --- ♻️ Found this useful? Share it with another builder. ➕ For daily practical AI and Python posts, follow Banias Baabe.

𝗖𝗿𝗲𝗮𝘁𝗲 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀! 🖥️ Machine learning models lack explainability, hence making their predictions difficult to interpret. This can be a significant challenge in regulated industries, where black box implementations are unacceptable. explainerdashboard is a Python library that helps you understand machine learning models by providing an interactive dashboard. The library supports various approaches, including SHAP values, permutation importances and dependence plots. Check the link below for more information, and make sure to follow me for regular data science content! 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗿𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝘀𝗶𝘁𝗲: https://lnkd.in/dfFkMGjH 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #datascience #python #deeplearning #machinelearning

Day 3: Understanding Python Data Types 🐍 Today I explored how Python organizes data — and it's simpler than I thought! Single-valued types: int, float, complex (numbers) bool (True/False) None (represents absence of value) Multi-valued types: Sequential: string, list, tuple, range Non-sequential: set, frozenset, dict Quick example: age = 21 # int gpa = 8.5 # float imaginary = 10+2j is_student = True # bool skills = ['Python', 'ML', 'AI'] # list names=('cat', 'dog', 'ant') #tuple collections = {1, 2, 3, 4}#set sample = {1:100, 2:200, 3:300} #dictionary Understanding data types helps me write cleaner code and avoid errors. Consistency beats perfection. Day 3 ✅ #Python #AI #MachineLearning #consistancy #LearnInPublic #StudentLife #TechJourney #learningpython