Niaz Chowdhury, PhD’s Post

Before data becomes vectors, it lives in Python structures. Lists, tuples, dictionaries, and sets are not just programming basics they are the backbone of AI pipelines. Lists handle raw datasets and batch inputs. Tuples ensure safe, immutable records. Dictionaries map features, configurations, and model outputs. Sets clean data by removing duplicates and building vocabularies. Together, they form the conceptual bridge from raw data to NumPy arrays and machine learning models. Mastering these structures means mastering the foundation of AI. #MachineLearning #Python #DataStructures #AIEngineering #LearningAI #TechCareers

The **AI Fundamentals** Bundle  🤖 Course 4 — Machine Learning: Primer Module GenAI is built on ML foundations. Clustering, classification, regression, and ML workflows including MLOps basics. #AIFundamentals #GenAI #MachineLearning #DataScience #Python #LearningAndDevelopment #Upskilling #Grokkers

Exploring data with Pandas 🐼 Learning how to clean, analyze, and transform datasets efficiently using Python. Every dataset tells a story — and I’m learning how to read it. #Python #Pandas #DataScience #AI #Analytics #DataAnalysis

“InsightFace Explained: From Images to Embeddings in Python Using Deep Learning” Most tutorials focus on how to use a library. In this article, I focused on something more important: 👉 Understanding how InsightFace works under the hood 👉 How images are converted into embeddings 👉 How face matching actually works 👉 And how to build a simple, production-style pipeline using Django I’ve broken everything down step by step—from uploading a selfie to retrieving matching photos from a database. If you’re working with: Computer Vision Machine Learning Python backend systems Or building real-world AI applications this might be useful for you. Would love your feedback 👇 Here is the link to read my article https://lnkd.in/gARtC9Ng #ArtificialIntelligence #MachineLearning #ComputerVision #Python #DeepLearning

Learn deep learning with Python and TensorFlow, including basics, key concepts, and practical applications, with expert perspectives and insights https://lnkd.in/dJ-gcFnK #DeepLearningWithPython Read the full article https://lnkd.in/dJ-gcFnK

Learn about deep learning with Python and get started with this comprehensive guide, including tutorials, examples, and code snippets using TensorFlow, Keras, and PyTorch https://lnkd.in/gheYKaSd #DeepLearningWithPython Read the full article https://lnkd.in/gheYKaSd

Learn about deep learning with Python and get started with this comprehensive guide, including tutorials, examples, and code snippets using TensorFlow, Keras, and PyTorch https://lnkd.in/gheYKaSd #DeepLearningWithPython Read the full article https://lnkd.in/gheYKaSd

Day 3 of my AIML journey 🚀 Started with Python basics… Now stepping into Machine Learning 🤖 Today I learned: → What is Machine Learning → Types of ML (Supervised vs Unsupervised) Still a bit confused 😅 Trying to understand it step by step Anyone learning AI like me? 👇 #AI #MachineLearning #Python #AIML #100DaysOfCode #LearningInPublic

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

Exploring Simple Linear Regression in Machine Learning I created a Kaggle notebook to understand how a linear relationship between variables can be modeled to make predictions. This project focuses on the fundamentals of building, interpreting, and evaluating a simple regression model, forming a strong foundation for more advanced ML techniques. Kaggle notebook 👇 https://lnkd.in/gX7CQAgi #MachineLearning #DataScience #LinearRegression #Kaggle #Python