ML Fundamentals

Kaggle's 30 Days Of ML (Competition Part-4): Hyperparameter tuning using Optuna

What is Interpretable Machine Learning - ML Explainability - with Python LIME Shap Tutorial

Affordable Machine Learning And Advance Deep Learning Course From iNeuron

Robot Dogs: A Programmer's Best Friend

Kaggle 30 Days of ML (Day 18) - SHAP - Shapley Values - Interpretable Machine Learning - XAI

Deep learning project end to end | Potato Disease Classification Using CNN - 1 : Problem Statement

How to Monitor Machine Learning Models (Evidently AI)

Tesla AI Day Highlights | Lex Fridman

Coding Linear Trees! : Data Science Code

Is Data Visualization Important for Data Science? (A Data Scientist's Perspective)

Why do we care about cross-correlations vs convolutions | ❓ #AICoffeeBreakQuiz #Shorts

Kaggle's 30 Days Of ML (Competition Part-3): What is Target Encoding and how does it work?

W&B Fastbook Reading Group — 11. Introduction to convolutions

Josh Bloom — The Link Between Astronomy and ML

Convolution vs Cross-Correlation. How most CNNs do not compute convolutions. | ❓ #Shorts

Four ways to examine the steps of a Pipeline

Computer Scientist Explains Machine Learning in 5 Levels of Difficulty | WIRED

FastAPI Tutorial | FastAPI vs Flask

If You Are Losing Motivation While Learning Data Science- Hear This Story Out️‍🔥️‍🔥️‍🔥️‍🔥️‍🔥

Python for Data Science | Beginner Friendly Full Course in 5 Hours

Save a model or Pipeline using joblib

Creating MLOps Standards // Alex Chung and Srivathsan Canchi // MLOps Coffee Sessions #50

SRGAN implementation from scratch

Kaggle 30 Days of ML (Day 17) - Partial Dependence Plot - Interpretable Machine Learning - XAI

Kaggle 30 Days of ML (Day 16) - Feature Importance of Machine Learning with ELI5

Kaggle's 30 Days Of ML (Competition Part-2): Feature Engineering (Categorical & Numerical Variables)

Object detection on an RC car with W&B and YOLOv5

Kaggle 30 Days of ML (Day 15) - Interpretable Machine Learning Use-cases

How to avoid Google Colab Session Runtime from Closing with JavaScript?

Kaggle's 30 Days Of ML (Competition Part-1): Cross Validation & First Submission on Kaggle

Kaggle 30 Days of ML (Day 14) - XGBoost, Data Leakage - Learn Python ML in 30 Days

Kaggle 30 Days of ML (Day 13) - Scikit-Learn Pipeline, CrossValidation - Learn Python ML in 30 Days

Kaggle's 30 Days Of ML (Day-14 Part-2): What is Data Leakage?

Kaggle's 30 Days Of ML (Day-14 Part-1): Intro to XGBoost

Kaggle 30 Days of ML - Day 12 - Kaggle Missing Values, Encoding - Learn Python ML in 30 Days

Kaggle's 30 Days Of ML (Day-13 Part-2): Cross-validation

Kaggle's 30 Days Of ML (Day-13 Part-1): Scikit-Learn Pipelines

Is age a barrier to learn data science?

Quantization in deep learning | Deep Learning Tutorial 49 (Tensorflow, Keras & Python)

Kaggle's 30 Days Of ML (Day-12 Part-1): Handling Missing Values in Datasets (imputing missing value)

Kaggle 30 Days of ML - Day 11 - Kaggle Competition Submission - Learn Python ML in 30 Days

W&B Fastbook Reading Group — 10. Movie recommendation using fastai

Kaggle's 30 Days Of ML (Day-11): Machine Learning Model to Predict House Prices (Intro to ML Ends)

Run VSCode In Web-Github Codespaces Introduction

Kaggle 30 Days of ML - Day 10 - Overfitting, Random Forest - Learn Python ML in 30 Days

Kaggle's 30 Days Of ML (Day-10): Underfitting, Overfitting & Random Forests

How do random forests work?

Kaggle's 30 Days Of ML (Day-9): First Machine Learning Model and Validation