Python Variables and Built-in Functions for AI Models

📘 Day 2 of My Machine Learning Journey 🚀 Today was all about building strong Python fundamentals, because no matter how advanced ML gets, everything starts here. 🔍 What I worked on today: ✅ Anaconda installation & environment setup ✅ Different ways to create virtual environments (and why they matter) ✅ Python basic syntax ✅ Variables & data types in Python ✅ Operators and how they actually work under the hood 💡 Key takeaway: Machine Learning isn’t just about models — it’s about writing clean, reliable, and understandable Python code. Strong basics today = fewer problems tomorrow. I’ll continue sharing my daily learnings, notes, and practical insights as I move forward. 👉 If you’re also learning Python, ML, or AI — or planning to start — feel free to follow along or share your experience in the comments. Day 2 done. On to Day 3 🔥 #MachineLearningJourney #LearningInPublic #Python #DataScience #AI #Upskilling #Consistency

🚀 Day 4/15: Intermediate to Advanced Python for ML/DL/AI Projects 🐍 Forgotten cleanups causing leaks in your training runs? 😤 Today: Context Managers & contextlib — automatic resource handling that always works. Beginner-friendly breakdown + 5 interview questions with code → swipe the carousel! Save if you're writing robust pipelines! What's your go-to context manager trick? Tell me below 👇 #Python #MachineLearning #DeepLearning #AI #DataScience #MLOps #Python #MachineLearning #DeepLearning #AI #DataScience #MLOps

Going to start sharing things I've learned building ML models from scratch over the past few months. First up: a readable GPT-2 implementation. ~800 lines of Python. For folks like me, who aren't classically trained but want to actually understand this stuff. Still amazes me that lines of code like this, can autocomplete sentances in English. https://lnkd.in/gXd3CSJ3

🚀 Day 38 – Machine Learning Journey Cross Validation (Python Implementation) Today I implemented Cross Validation in Python to evaluate models more reliably instead of trusting a single train–test split. 🔹 Why in code Measures consistency, not luck Reduces overfitting bias Gives a stable performance estimate 🐍 Minimal Python example (scikit-learn) from sklearn.model_selection import KFold, cross_val_score from sklearn.linear_model import LogisticRegression model = LogisticRegression() kf = KFold(n_splits=5, shuffle=True, random_state=42) scores = cross_val_score(model, X, y, cv=kf) print(scores.mean()) 🔹 What this does: Splits data into 5 folds Trains on 4, validates on 1 Repeats for all folds Reports the average score 🔑 Key takeaway: > Cross validation doesn’t change the model — it validates whether the model can be trusted. #Day38 #MachineLearning #CrossValidation #Python #ScikitLearn #LearningJourney #ModelEvaluation #DataScience #AI #LearningJourney

🚀 Built Logistic Regression From Scratch (No ML Libraries!) Today I implemented a Logistic Regression model from scratch using Python & NumPy to truly understand how classification models work. 📌 Key concepts I explored: Sigmoid function which is an activation function of this model. Gradient Descent for updating weights and bias. Cross-Entropy / Log Loss as the loss function for classification. It is used as a classification model. (Probability lies between 0 and 1). Note : Mean Squared Error can't be used as the loss function for logistic regression since it causes gradient descent with multiple local minima. I’d really appreciate feedback or suggestions on my approach — especially around optimization or best practices. Always open to learning! 🙏 Github repository link : https://lnkd.in/gfsDug8U #MachineLearning #LogisticRegression #FromScratch #Python #NumPy #DataScience #AI #LearningByDoing #Placements

My latest Machine Learning project involved Python and Logistic Regression. 🔍 Project: BBC News Classification 📊 Goal: Classify news articles as short or long based on description length 💡 What I learned: • How Machine Learning works end-to-end • Feature engineering and data preprocessing • Train/test split and model evaluation • Logistic Regression fundamentals • Visualizing predictions and errors This project helped me understand the difference between creating a model, training it, and evaluating its performance. 🔗 GitHub: https://lnkd.in/dqRPSjZQ #MachineLearning #Python #DataScience #LearningByDoing #AI

Day 22 & 23 | AI/ML Learning Journey | Python —Pandas Topic: Pandas (Practice) Over the last two days, I focused on Pandas fundamentals by working with real datasets. What I covered: •DataFrame methods — head(),tail(),info(), describe() etc. •Loading datasets from Kaggle •Data selection — iloc(position) , loc(label) •Filtering & Query filter •Data cleaning techniques  • Handling missing values  • Removing duplicates  • Converting data types Consistency Challenges. #AIML #DataScience #Pandas #Python #MachineLearning #LearningJourney #Kaggle #DataCleaning

Stop using pip for your Python projects. It's 2026, and the game has changed. 🐍🚀 Check out the full video here: https://lnkd.in/gbUGnjxf If you are still managing environments the "old way," you're likely dealing with slow install times, dependency hell, and bloated workflows. For anyone entering the Generative AI and LLM space, these bottlenecks are project-killers. In the debut episode of my new series, Gen AI – 101, I break down why I've officially switched to uv by Astral. Why UV? ⚡ Speed: It’s written in Rust and is 10x–100x faster than pip. 📦 Simplicity: It replaces pip, venv, and even python version management. 🛠️ Reliability: Built for the complex dependency trees we see in modern AI libraries. This isn't just about a new tool; it's about building a professional-grade foundation for your AI journey. I designed this episode specifically for students and early-career developers who want to skip the "theory overload" and get straight to a clean, practical setup. Let's build something smarter. 🤖 #Python #GenAI #SoftwareEngineering #DataScience #UV #WebDevelopment #ArtificialIntelligence #CodingTips #aimanabhashalo

Machine learning often gets treated like a futuristic concept, but it’s already embedded in everyday tools: search engines, photo tagging, spam filtering, and countless industry workflows. More people than ever want to learn ML, yet many beginners hit a wall when they encounter the heavy, bottom‑up approach most courses take. This blog post offers a more approachable entry point: start with Python. #MachineLearning #Python #AI #BeginnerDev #DataScience #RheinwerkComputingBlog #RheinwerkComputingInfographic Read here to learn how: https://hubs.la/Q040jR030

🚀 Starting Your AI Journey? Don’t Skip This Step. I’ve just published Video #002 in my AI Engineer Roadmap series, where I walk through a complete Python setup for AI beginners — step by step, hands-on, and beginner-friendly. 🎯 In this video, you’ll learn: How to install Python correctly How to set up VS Code for Python How to install & use Jupyter Notebook How to verify everything works before moving into AI & ML If you’re planning to learn Artificial Intelligence, Machine Learning, or Data Science, a clean Python setup is the first non-negotiable step — and this video saves you hours of confusion. 📺 Watch here: 👉 https://lnkd.in/g-dzkig6 This is part of a structured roadmap where I’m sharing practical, no-fluff guidance for people who want to become AI Engineers from scratch. 🙏 If you find the video useful: Please like & share it with someone starting out Subscribe to Codetician on YouTube for upcoming AI + Python videos Comment “AI Ready” once your setup is done 🚀 Would love to hear: 👉 Are you just starting with AI, or already working in the field? #Python #ArtificialIntelligence #MachineLearning #AIEngineer #LearnPython #DataScience #CareerInAI #Codetician #AIEngineerRoadmap