Krish Naik Courses

Performance Metrics for Classification Problem in Machine Learning - Part 1

Learn key performance metrics for binary classification in machine learning, including recall and precision, to effectively evaluate model performance and make informed decisions.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

Pandas Tutorial - Reading CSV Files with Various Parameters - Part 2

Learn to use Pandas for reading CSV files with various parameters, exploring functions and customization options to efficiently handle and analyze data in Python.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Numpy and Inbuilt Functions Tutorial

Learn NumPy library essentials, including arrays, indexing, and built-in functions, to enhance your Python skills for data manipulation and scientific computing.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Python List and Boolean Variables

Learn Python lists and boolean variables in this concise tutorial covering key concepts, operations, and built-in functions for effective programming.

• YouTube
• 22 minutes
• Self-Paced
• Free Video

Create Artificial Neural Network Using Weight Initialization Tricks

Learn weight initialization techniques for creating artificial neural networks, enhancing model performance and convergence in deep learning applications.

• YouTube
• 25 minutes
• Self-Paced
• Free Video

End to End ML Project With Deployment - Github and Code Set Up

Comprehensive guide to setting up an end-to-end machine learning project, covering GitHub repository creation, environment setup, and initial code commits.

• YouTube
• 39 minutes
• Self-Paced
• Free Video

How to Deploy End to End ML Projects in Production AWS Cloud Using CI CD Pipeline

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.

• YouTube
• 46 minutes
• Self-Paced
• Free Video

End to End ML Project - Data Transformation Implementation Using Pipelines

Implement data transformation using pipelines for ML projects, covering categorical and missing value handling, standard scaling, and artifact storage.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Docker for Data Science Tutorial

Comprehensive Docker tutorial for data scientists, covering containers, images, installation, and Docker Compose. Learn to implement end-to-end data science projects using Docker.

• YouTube
• 1 hour 19 minutes
• Self-Paced
• Free Video

End to End MLOPS Data Science Project Implementation With Deployment

Comprehensive MLOps project implementation covering data ingestion, validation, transformation, model training, and deployment on AWS EC2 using MLflow and GitHub Actions.

• YouTube
• 2 hours 33 minutes
• Self-Paced
• Free Video

Evidently AI Tutorial - Open Source ML Models Monitoring and Observability

Learn to monitor and evaluate ML models with Evidently AI, an open-source Python library for data scientists. Explore reports, test suites, and dashboards for continuous model quality assessment.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Step-by-Step Machine Learning Project Implementation: EDA, Feature Engineering, Selection, and Model Training

Comprehensive guide to implementing a machine learning project, covering data cleaning, EDA, feature engineering, selection, model training, and hyperparameter tuning.

• YouTube
• 1 hour 26 minutes
• Self-Paced
• Free Video

BentoML Tutorial - Build Production Grade AI Applications

Learn to build scalable AI applications with BentoML, covering implementation, model serving, API demo, and packaging for production deployment.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

End-to-End ML Project with Deployment - Project Structure, Logging, and Exception Handling - Tutorial 2

Learn to structure, log, and handle exceptions in an end-to-end machine learning project, focusing on practical implementation and deployment techniques.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Inside ChatGPT- Unveiling the Training Process of OpenAI's Language Model

Explore the training process behind ChatGPT, including generative pretraining, supervised fine-tuning, and reinforcement learning through human feedback. Gain insights into AI language model development.

• YouTube
• 21 minutes
• Self-Paced
• Free Video