#KB Deep Learning 2 — Fundamentals of StatisticsUnderstanding statistics is essential for deep learning. Learn how probability, sampling, and inference impact model performance &…Feb 41Feb 41
#KB Deep Learning I — Foundations, Applications, and Modern ImpactA Comprehensive Introduction to Deep Learning: Core Concepts, Real-World Applications, and Future Challenges in AI.Jan 31Jan 31
#KB Sentiment Analysis — Rule-Based vs. ML ApproachesSentiment analysis turns text into insights, using tokenization, polarity scores, and models like VADER and Roberta for better accuracy.Nov 30, 2024Nov 30, 2024
#KB Amazon Redshift — Analytics at ScaleExplore Amazon Redshift’s ability to handle massive datasets using parallel processing, columnar storage, and flexible distribution styles.Nov 30, 2024Nov 30, 2024
#KB Alteryx — Case Study Insights from a DatathonBuild efficient workflows in Alteryx to clean and analyze data, as seen in the 2024 SoCal Datathon case study.Nov 30, 2024Nov 30, 2024
#KB AWS Fundamentals — ML with SageMakerLearn how to set up SageMaker, create a notebook, and train your first ML model using AWS tools and services.Nov 24, 2024Nov 24, 2024
#KB Probability Theory — Part 5- The Central Limit TheoremCentral Limit Theorem explained: From sample sizes to practical limits, see why it’s essential in statistical analysis and business.Nov 10, 2024Nov 10, 2024
#KB AWS Fundamentals — Compute Cloud with EC2Learn to deploy, monitor, and optimize Shiny applications on AWS EC2 with this comprehensive and practical setup guide.Nov 6, 2024Nov 6, 2024
#KB Probability Theory — Part 4- Common Continuous Probability DistributionsUse Uniform and Exponential distributions to solve real-world business problems with probability theory basics.Oct 31, 2024Oct 31, 2024
#KB App Development — UI and Dashboard Design with Shiny IIExplore UI principles for Shiny app development, from wireframing to implementing responsive dashboards, with a stock portfolio as example.Oct 31, 2024Oct 31, 2024
