📊 A practical toolkit for feature engineering and selection in Python. Explore EDA, handle missing data and outliers, scale/encode/transform features, discretize, and select via filter, wrapper, embedded, shuffling, and hybrid methods. Comes with datasets, clean notebooks, reusable utils, and clear visuals for fast, reproducible ML. Workflows.

Trustworthy AI: From Theory to Practice book. Explore the intersection of ethics and technology with 'Trustworthy AI: From Theory to Practice.' This comprehensive guide delves into creating AI models that prioritize privacy, security, and robustness. Featuring practical examples in Python, it covers uncertainty quantification, adversarial ML

A comprehensive repository documenting my Machine Learning learning journey with detailed notes and practical code implementations. This repo covers fundamental ML concepts, algorithms, and hands-on coding in Python, NumPy, Pandas, Scikit-Learn, TensorFlow, and more. Perfect for learning, revision, and interview prep!

Comprehensive Python ML repository with 50+ Jupyter notebooks covering neural networks, GNNs, transformers, AutoML, time series analysis, computer vision, and medical AI applications. Practical examples from basic algorithms to cutting-edge architectures.

🤖📊 A fun and interactive Machine Learning repo packed with simple explanations, visual examples, and practical Python implementations to make learning ML enjoyable and approachable.

Discover ML projects with Scala & Python. Explore data analysis, MLflow integration, regression, decision tree classification, Spark DataFrame manipulation, flight & retail sales analysis, and statistical utilities. Includes datasets like forestfires and online shoppers intention for practical learning.

A practical FastAPI-based repository for building and deploying APIs, starting from web and database projects to serving ML, DL, and LLM models. Ideal for learning scalable AI deployment and modern API design with Python, Pydantic, and production-ready tools like LangChain and HuggingFace.

Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills. The Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages who want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud. Each of the 10 weeks features a comprehensive lab developed specifically for this Specialization that provides hands-on experience with state-of-the-art algorithms for natural language processing (NLP) and natural language understanding (NLU), including BERT and FastText using Amazon SageMaker. Applied Learning Project By the end of this Specialization, you will be ready to: • Ingest, register, and explore datasets • Detect statistical bias in a dataset • Automatically train and select models with AutoML • Create machine learning features from raw data • Save and manage features in a feature store • Train and evaluate models using built-in algorithms and custom BERT models • Debug, profile, and compare models to improve performance • Build and run a complete ML pipeline end-to-end • Optimize model performance using hyperparameter tuning • Deploy and monitor models • Perform data labeling at scale • Build a human-in-the-loop pipeline to improve model performance • Reduce cost and improve performance of data products

PySpark ML Examples: Practical Python notebooks for machine learning with PySpark, covering classification, regression, and clustering. Includes a Dockerfile for easy setup.

A Practical Guide for Running a Python Project Inside a Docker Dev Container Inside an Azure ML VM Connected with Local VS Code

This interactive website offers comprehensive Machine Learning notes, blending theoretical explanations with practical Python code examples. It covers foundational to advanced ML algorithms, featuring mathematical formulas, step-by-step calculations, and easily runnable code via Google Colab. Ideal for students and enthusiasts.

Machine Learning repo based on the CodeBasics YouTube playlist. Covers core ML concepts with hands-on Python using pandas and scikit-learn: linear & logistic regression, SVM, decision trees, random forest, model evaluation, and train/test workflows. Clean notebooks and structured code for practical learning.

"This repository helps you learn Python and build a strong foundation in AI and ML through hands-on projects. It is ideal for students, beginners starting a tech career, and professionals looking to switch domains or strengthen their skills with practical, real-world experience, guided examples, and industry-relevant concepts."

My final project submission for the course IBM: ML0101EN Machine Learning with Python: A Practical Introduction

ML with python in practical applications

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A collection of bite-sized machine learning projects showcasing practical applications, built from scratch with gradient descent and Python. Perfect for learning and experimenting with ML fundamentals! 🌶️

ML-Training is a collection of Python scripts and notebooks covering basic programming and machine learning concepts, with practical examples like real estate price prediction.

Centralized learning repository with syllabus-aligned notes and practicals for Mathematics for Machine Learning, Advanced Algorithms in AI & ML, and Big Data Analytics. Covers AI, ML, Deep Learning, Hadoop, Spark, and Python-based labs.

Python ML Coding Practices 🚀 Enhance your Python machine learning skills with practical coding examples, tips, and best practices. Dive into hands-on exercises covering data preprocessing, model training, evaluation, and deployment.

MathML: Mathematical Foundations for Machine Learning A Python library that extends NumPy, SciPy, and Pandas with ML-focused mathematical functions, statistical methods, and linear algebra utilities. Designed to bridge the gap between theoretical ML mathematics and practical implementation.

ML project using Python & scikit-learn to classify flower species based on petal & sepal measurements. Enhance data analysis & pattern recognition skills. Explore botany secrets with practical insights. Dive into ML algorithms & unravel iris species. #MachineLearning 🌺🌿 @BharatIntern

Explore vector databases and AI with Python, Pinecone, LangChain, OpenAI, and Hugging Face. This repo demonstrates building AI and ML-driven chat applications, showcasing practical implementations and innovative chatbot solutions.

A practical toolkit of Python scripts for data preprocessing in ML. Covers data cleaning, feature scaling, & encoding with Pandas and Scikit-learn. Essential steps to transform raw data for building accurate models.

Python repo showcasing hands-on implementations of advanced ML algorithms. It includes practicals on neural networks, Gaussian mixture models, Naive Bayes, and generative vs discriminative models, along with sample datasets and visualizations.

Hands-on Python and notebook examples illustrating gradient descent, linear regression, and data visualization from scratch and with scikit-learn. Includes clear code, visualizations, and CSV data for practical understanding of optimization and ML fundamentals.

A Movie Recommendation System that suggests personalized movies using content-based and collaborative filtering techniques. Built with Python and ML libraries, it demonstrates practical application of data analysis and machine learning in real-world scenarios.

EcoFlow AI uses practical generative AI and ML to optimize cement plant operations. Features include real-time monitoring, energy optimization, predictive maintenance via NLP, and waste management with computer vision. Built with Python, Flask, React, TensorFlow, and Google Cloud APIs.

Collection of practical data science projects demonstrating end-to-end analytics workflow: from data wrangling and EDA to ML modeling and interactive visualization. Built with Python, SQL, Tableau, and QuickSight to solve real-world business problems.

🚀 Hands-on Machine Learning projects | 📊 Regression, Classification, Visualization & more! 🤖 ML Playground – Learn, Build & Experiment with Machine Learning models 📈 📚 Beginner to Advanced Machine Learning | 🐍 Python + Scikit-learn + Jupyter 🌟 Practical ML Projects – From Data Cleaning 🧹 to Predictions 🔮 ⚡ Machine Learning Made Simple