ML Fundamentals

9.7 The .632 and .632+ Bootstrap methods (L09 Model Eval 2: Confidence Intervals)

9.6 Bootstrap Confidence Intervals (L09 Model Eval 2: Confidence Intervals)

Talks # 14: Martin Henze; Knowledge is Power: Understanding your Data through EDA and Visualisations

Osmo Wonderland - Perfect Christmas Gift For Kids

Getting ready to learn Python, Windows edition #1: Files and directories

Pytorch Conditional GAN Tutorial

Set a "random_state" to make your code reproducible

Ep#2 - What are regulations saying about data privacy?

How Deep Learning has Revolutionized OCR with Cha Zhang - #416

Add a missing indicator to encode "missingness" as a feature

The ROC Curve : Data Science Concepts

Flow Physics Quantification in an Aneurysm Using NVIDIA PhysicsNeMo

Live at Jeff Kinney’s pool party and The Deep End book review!

Joe Spisak-Deep Learning Development with PyTorch+Jupyter using Heterogenous hardware|JupyterCon2020

Important Steps I Have Followed To Improve My Data Science Skills- Sharing My Experience

When Machine Learning meets privacy - Episode 1

Why There Are So Many Start Ups In AI, ML And DL? Important For Everyone

This Book will Help you Land a Data Science Job

Curse of Dimensionality : Data Science Basics

How to Build Classification Models (Weka Tutorial #2)

WGAN implementation from scratch (with gradient penalty)

Types Of Machine Learning | Machine Learning Algorithms | Machine Learning Tutorial | Simplilearn

Jetson AI Fundamentals - S3E4 - Object Detection Inference

Backpropagation Details Pt. 2: Going bonkers with The Chain Rule

Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.

Charles Isbell: Computing, Interactive AI, and Race in America | Lex Fridman Podcast #135

Data augmentation to address overfitting | Deep Learning Tutorial 26 (Tensorflow, Keras & Python)

🥕 Integrate Weights & Biases with Keras

Getting ready to learn Python, Mac edition #5: Write and run a Python program

DeepLearning.AI Learner Community Event ft. Ghaith Sankari

DeepLearning.AI Learner Community Event ft. Jan Jitse Venselaar

9.5 Resampling and Repeated Holdout (L09 Model Eval 2: Confidence Intervals)

9.4 ML Confidence Intervals via Normal Approximation (L09 Model Eval 2: Confidence Intervals)

9.3 Holdout Model Selection (L09 Model Eval 2: Confidence Intervals)

9.2 Holdout Evaluation (L09 Model Eval 2: Confidence Intervals)

9.1 Introduction (L09 Model Eval 2: Confidence Intervals)

8.5 Bias-Variance Decomposition of the 0/1 Loss (L08: Model Evaluation Part 1)

Use Pipeline to chain together multiple steps

8.4 Bias and Variance vs Overfitting and Underfitting (L08: Model Evaluation Part 1)

8.3 Bias-Variance Decomposition of the Squared Error (L08: Model Evaluation Part 1)

8.2 Intuition behind bias and variance (L08: Model Evaluation Part 1)

8.1 Intro to overfitting and underfitting (L08: Model Evaluation Part 1)

Handle unknown categories with OneHotEncoder by encoding them as zeros

DCGAN implementation from scratch

Difficulties I Faced As A Data Scientist-Sharing My Experience From Start

Building our first simple GAN

How to Build Regression Models (Weka Tutorial #1)

An Introduction to Generative Adversarial Networks (GANs)