AI, Machine Learning and Deep Learning
Explore AI, machine learning, and deep learning concepts, including supervised, unsupervised, and reinforcement learning paradigms. Learn when to use traditional ML or deep neural networks for audio applications.
Tabular Data
Learn machine learning techniques for tabular data, including model evaluation, feature engineering, tree-based models, hyperparameter tuning, and neural networks using AWS SageMaker.
Building Data Infrastructure at Scale for AI/ML with Open Data Lakehouses
Explore how data lakehouse architecture with Apache Hudi supports real-world predictive ML and vector-based AI use cases in this 30-minute keynote by Vinoth Chandar, creator of Apache Hudi. Learn about ingesting data with minute-level freshness, providin…
Data Engineering for Streamlining the Data Science Developer Experience - DE4AI
Explore the challenges and solutions in data engineering for optimizing the data science developer experience in this 12-minute talk by Aishwarya Joshi from Chime. Discover how to enable efficient feature engineering and deployment for low-latency infere…
Quick Deploy Model Serving in Ranking Systems
Discover how to streamline model deployment in ranking systems using ElasticSearch plugins and MLeap bundles for efficient, error-free updates and seamless integration with existing infrastructure.
Essential Workshop to Exploratory Data Analysis and Feature Engineering
Explore and preprocess data, analyze correlations and distributions, engineer features, and train ML models using a Google Play Store dataset. Improve model accuracy and efficiency.
Avoid ML OOps with ML Ops - A Modular Approach to Scaling End-to-End ML Platforms
Discover a step-by-step approach to building a scalable ML infrastructure. Learn to identify key areas for improvement, implement modular components, and enhance your end-to-end ML lifecycle for efficient, automated operations.
Learning from Extremes: What Fraud-Fighting at Scale Can Teach Us About MLOps Across Domains
Explore how anti-fraud MLOps practices can simplify architectures, enhance observability, and boost agility across various ML domains.
How to Design and Build Resilient Machine Learning Systems
Explore strategies for building resilient ML systems that gracefully handle real-world challenges. Learn to detect, mitigate, and overcome production-specific risks across various domains.
Feature Engineering Made Simple
Master feature engineering techniques to streamline your machine learning workflow. Explore systematic approaches using Anovos, an open-source library for efficient data processing and model building.
UnionML: A Microframework for Building Machine Learning Applications
Explore UnionML, a microframework simplifying ML application development. Learn to streamline the process from research to production, addressing common challenges in the ML lifecycle.
Building Real-Time ML Features with a Feature Platform
Explore challenges in deploying ML pipelines and learn how feature stores and platforms solve data problems for production ML, enabling real-time processing and scaling.
The Critical Things You Have to Build to Transform Your Company to be ML Driven
Discover key components for transforming your company into an ML-driven organization. Learn strategies for overcoming challenges in the machine learning development lifecycle and deploying models effectively.
NLP in Python - Natural Language Processing
NLP is a field concerned with the ability of a computer to understand, analyze, manipulate and potentially generate human language.In this course we study about NLP and use the NLP toolkit or NLTK in Python.
Multivariate Time Series Forecasting in R - Data Analytics with R - Data Science
Learn multivariate time series forecasting in R, covering fundamentals, modeling techniques, feature engineering, and practical implementation through a hands-on demo.
Feature Engineering
Learn to optimize machine learning models through feature engineering techniques, from basic concepts to practical Python demonstrations, enhancing your data science skills.
EDA and Feature Engineering Playlist
Hands-on sessions exploring data analysis and feature engineering using real-world datasets, covering Zomato, Black Friday sales, flight prices, and automated EDA techniques.
Feature Engineering
Learn essential feature engineering techniques for data preprocessing, including encoding, scaling, handling missing values, and addressing imbalanced datasets to improve machine learning model performance.
Machine Learning Pipelines
Explore advanced house price prediction through data analysis, feature engineering, and selection techniques for improved model accuracy.
Exploratory Data Analysis
Learn advanced techniques for exploratory data analysis and feature engineering in house price prediction, including automated visualization tools and efficient EDA libraries.
Introduction to Regression Models by Using R and Tidymodels
Explore regression models using R and tidymodels to predict numeric values. Learn when to use regression, train models, and evaluate performance through hands-on bike rental prediction.