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01 - Introduction\ 0:00 Introduction 5:03 Course Guide 6:17 Setting Up and Installing Packages & Course Materials 12:23 What is Version Control 16:27 Importance of Version Control 18:06 Data Version Control - The What and Why 22:52 Version Control Tools 24:01 Project Structuring Using Cookiecutter 02 - Git Essentials For Data Science\ 36:54 What is Git 39:56 Git Workflow - Theory 43:29 Configuring Git 46:36 Github Platform 53:35 Configuring SSH For GitHub 59:46 Git Essentials - Creating a Repo 1:06:08 Git Workflow - Practical 1:20:01 Git Essentials - Commit & Best Practices 1:33:40 Git Essentials - Undoing Uncommitted Changes 1:38:37 Git Essentials - Exploring Git Commit on Github 1:41:28 Git Essentials - Git Logs 1:51:27 Git Essentials - Branching For ML Model and Data Science 1:59:05 Git - Tricks & Tips 2:04:22 GitHub - Advanced Search 03 - Building A CLI for Version Control From Scratch\ 2:20:00 Intro and Designing of CLI for Version Control 3:35:37 Building Version Control CLI - Status Functionality 4:03:08 Building Version Control CLI - Push Functionality 4:14:57 Building Version Control CLI - Remove,Restore,Clone 03 - DVC Essentials\ 4:41:44 What is DVC Tool 4:45:36 DVC Features and Benefits 4:46:51 DVC - The 3 Areas of DVC 4:50:27 Advantages of Data Version Control 4:51:30 DVC vs Git Commands 4:53:38 DVC Essentials - Workflow 5:09:50 DVC Essentials - Pushing Your Data to GDrive 5:17:38 DVC Essentials - Pushing Data to Local Storage 04 - DAGsHub\ 5:25:54 DAGsHub Platform Walkthrough 5:32:42 DAGsHub - Creating a Repo 5:37:11 DAGsHub - Searching on the Platform 5:43:25 DAGsHub - Adding topics 5:46:48 DAGsHub - Label Studio 04 - End to End Data Science Project with DVC and Git\ 6:05:56 Intro & Setting up Workspace 6:21:48 Data Versioning and Pushing Data to Dagshub 6:33:48 Data Preparation & Model Building 6:56:05 Git Branching for ML Models 7:03:42 Model Storage on DagsHub with Git & DVC 7:13:50 Saving New ML Models to a New Branch 7:24:55 ML Experiment Tracking with DagsHub Pipelines - Makefiles for Data Science Project\ 7:43:52 Introduction & Manually Running ML Pipelines 7:51:03 DVC Pipelines - DVC Run Interactive Experiment 8:00:27 DVC Pipelines - DVC Run 8:12:37 DVC Pipelines - DVC Metrics 8:16:36 DVC Pipelines - DVC Repro 8:29:32 DVC Pipelines - Pushing Data and Code to DagsHub 8:42:39 DVC Pipelines - Fixing Error with Push and Pull
