Learn about seven different database paradigms and what they do best.

Contents:

  • 00:00 Intro
  • 00:45 Key-value
  • 01:48 Wide Column
  • 02:47 Document
  • 04:05 Relational
  • 06:21 Graph
  • 07:22 Search Engine
  • 08:27 Multi-model

In the last several months, MLflow has introduced significant platform enhancements that simplify machine learning lifecycle management.

Expanded autologging capabilities, including a new integration with scikit-learn, have streamlined the instrumentation and experimentation process in MLflow Tracking.

Additionally, schema management functionality has been incorporated into MLflow Models, enabling users to seamlessly inspect and control model inference APIs for batch and real-time scoring. 

Learn the basics of Data Science in the crash course created by Marco Peixeiro, where you will learn about the theory and code behind the most common algorithms used in data science.

Datasets:

Course Contents

  • ⌨️ (00:00) Introduction
  • ⌨️ (03:06) Setup
  • ⌨️ (04:29) Linear regression (theory)
  • ⌨️ (09:29) Linear regression (Python)
  • ⌨️ (20:59) Classification (theory)
  • ⌨️ (30:16) Classification (Python)
  • ⌨️ (49:30) Resampling & regularization (theory)
  • ⌨️ (56:09) Resampling and regularization (Python)
  • ⌨️ (1:05:17) Decision trees (theory)
  • ⌨️ (1:13:12) Decision trees (Python)
  • ⌨️ (1:24:50) SVM (theory)
  • ⌨️ (1:28:17) SVM (Python)
  • ⌨️ (1:58:24) Unsupervised learning (theory)
  • ⌨️ (2:06:54) Unsupervised learning (Python)
  • ⌨️ (2:20:55) Conclusion