Courses from 1000+ universities
India banned Telegram after the NEET paper leak led to a retest for 2.28 million students. Class Central studied the scam, the money trail, and other platforms the leaks could move to.
600 Free Google Certifications
Artificial Intelligence
Language Learning
Data Analysis
Mathematical and Computational Methods
AP® Microeconomics
Competitive Strategy
Organize and share your learning with Class Central Lists.
View our Lists Showcase
Learn NumPy library essentials, including arrays, indexing, and built-in functions, to enhance your Python skills for data manipulation and scientific computing.
Learn Python lists and boolean variables in this concise tutorial covering key concepts, operations, and built-in functions for effective programming.
Learn weight initialization techniques for creating artificial neural networks, enhancing model performance and convergence in deep learning applications.
Comprehensive Docker tutorial for data scientists, covering containers, images, installation, and Docker Compose. Learn to implement end-to-end data science projects using Docker.
Comprehensive guide to building and deploying a machine learning project, covering problem definition, exploratory data analysis, feature engineering, and model training.
Comprehensive guide to building and deploying a deep learning project for chicken disease classification using MLOps tools, DVC pipeline, and cloud platforms Azure and AWS.
Comprehensive review of linear regression, covering simple and multiple regression, cost function, and convergence algorithms with mathematical intuition and practical examples.
Comprehensive tutorial on anomaly detection techniques in machine learning, covering Isolation Forest, DBSCAN clustering, and Local Outlier Factor with practical implementations and examples.
Deploy ML projects on AWS using CI/CD pipeline, ECR, and EC2. Learn to set up Docker, IAM, and App Runner for seamless end-to-end deployment of machine learning applications in the cloud.
Learn to structure, log, and handle exceptions in an end-to-end machine learning project, focusing on practical implementation and deployment techniques.
Comprehensive guide to setting up an end-to-end machine learning project, covering GitHub repository creation, environment setup, and initial code commits.
Implement data transformation using pipelines for ML projects, covering categorical and missing value handling, standard scaling, and artifact storage.
Learn to build scalable AI applications with BentoML, covering implementation, model serving, API demo, and packaging for production deployment.
Comprehensive guide to implementing a machine learning project, covering data cleaning, EDA, feature engineering, selection, model training, and hyperparameter tuning.
Comprehensive MLOps project implementation covering data ingestion, validation, transformation, model training, and deployment on AWS EC2 using MLflow and GitHub Actions.
Get personalized course recommendations, track subjects and courses with reminders, and more.