📐 ML Fundamentals

Neural networks, backpropagation, gradient descent — the maths behind AI

ML Fundamentals ⚡ AI Lesson

AdaBoost : Data Science Concepts

ritvikmath Intermediate 5y ago

ML Fundamentals

Data Science and Employee Productivity with Nicki | 365 Data Use Cases

365 Data Science Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Debug your YOLOv5 experiments with Weights & Biases

Weights & Biases Advanced 5y ago

ML Fundamentals

How to Perform Large-Scale Image Classification | Grandmaster Series E2

NVIDIA Developer Advanced 5y ago

ML Fundamentals

Impute missing values using KNNImputer or IterativeImputer

Data School Beginner 5y ago

ML Fundamentals

Loss Functions : Data Science Basics

ritvikmath Beginner 5y ago

ML Fundamentals

Ineuron's Detailed Course Content Discussion Of ML And DL- Affordable AI Education

Krish Naik Intermediate 5y ago

ML Fundamentals

Build a Voice Assistant using Javascript w/Tensorflow | For Beginners

CoderOne Beginner 5y ago

ML Fundamentals

Getting ready to learn Python, Windows edition #5: Writing and running Python program

Brandon Rohrer Beginner 5y ago

ML Fundamentals

Getting ready to learn Python, Windows edition #4: Installing and running Python

Brandon Rohrer Beginner 5y ago

ML Fundamentals

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

Sebastian Raschka Beginner 5y ago

ML Fundamentals

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

Sebastian Raschka Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

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

Abhishek Thakur Beginner 5y ago

ML Fundamentals

Robert Nishihara — The State of Distributed Computing in ML

Weights & Biases Intermediate 5y ago

ML Fundamentals

Osmo Wonderland - Perfect Christmas Gift For Kids

NPStation Beginner 5y ago

ML Fundamentals

Pytorch Conditional GAN Tutorial

Aladdin Persson Beginner 5y ago

ML Fundamentals

Set a "random_state" to make your code reproducible

Data School Beginner 5y ago

ML Fundamentals

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

MLOps.community Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

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

The TWIML AI Podcast with Sam Charrington Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Let's Build a Language Translator! LIVE

Siraj Raval Intermediate 5y ago

ML Fundamentals

Flow Physics Quantification in an Aneurysm Using NVIDIA PhysicsNeMo

NVIDIA Developer Beginner 5y ago

ML Fundamentals

Late Night Talks-Live Q&A-Ask Anything Related Data Science

Krish Naik Intermediate 5y ago

ML Fundamentals

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

NPStation Beginner 5y ago

ML Fundamentals

Chia liang Kao - Practical Enterprise JupyterHub Deployment and MLOps with PrimeHub| JupyterCon 2020

JupyterCon Intermediate 5y ago

ML Fundamentals

Karla Spuldaro - Building AI Pipelines with Elyra | JupyterCon 2020

JupyterCon Intermediate 5y ago

ML Fundamentals ⚡ AI Lesson

AIDEN physio assistant by Shivay Lamba - Made with TensorFlow.js

TensorFlow Intermediate 5y ago

ML Fundamentals

When Machine Learning meets privacy - Episode 1

MLOps.community Beginner 5y ago

ML Fundamentals

Getting ready to learn Python, Windows edition #3: Creating and editing text files

Brandon Rohrer Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

Getting ready to learn Python, Windows edition #2: The command prompt

Brandon Rohrer Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

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

Sebastian Raschka Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

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

Sebastian Raschka Beginner 5y ago

ML Fundamentals

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

Sebastian Raschka Beginner 5y ago

ML Fundamentals

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

Brandon Rohrer Beginner 5y ago

ML Fundamentals

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

Sebastian Raschka Beginner 5y ago

ML Fundamentals

9.1 Introduction (L09 Model Eval 2: Confidence Intervals)

Sebastian Raschka Beginner 5y ago

ML Fundamentals

TFIDF : Data Science Concepts

ritvikmath Intermediate 5y ago

ML Fundamentals ⚡ AI Lesson

Charles Frye on using machine learning to multiply by one

Weights & Biases Intermediate 5y ago

ML Fundamentals

Chirag Agarwal on detecting out-of-distribution data with Variance-of-Gradient

Weights & Biases Advanced 5y ago

ML Fundamentals

⚡ Supercharge your Training with PyTorch Lightning + Weights & Biases

Weights & Biases Intermediate 5y ago

ML Fundamentals

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

Data School Beginner 5y ago

ML Fundamentals ⚡ AI Lesson

The ROC Curve : Data Science Concepts

ritvikmath Beginner 5y ago

ML Fundamentals

Saturday Live Q&A Ask Anything Related Data Science

Krish Naik Intermediate 5y ago

ML Fundamentals

Important Interview Questions On Convolution Neural Network- Deep Learning

Krish Naik Intermediate 5y ago

ML Fundamentals

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

JupyterCon Beginner 5y ago

ML Fundamentals

Praveen Kanamarlapud - Kernel Life Cycle Management | JupyterCon 2020

JupyterCon Intermediate 5y ago

ML Fundamentals

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

Krish Naik Beginner 5y ago

ML Fundamentals

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

Sebastian Raschka Beginner 5y ago

ML Fundamentals

Use Pipeline to chain together multiple steps

Data School Beginner 5y ago

📚 Coursera Courses Opens on Coursera · Free to audit

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Advanced Deployment Scenarios with TensorFlow

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Google Cloud Speech API: Qwik Start

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AI Optimization & Experimental Methods

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📚 Coursera Course ↗

Self-paced

Browser-based Models with TensorFlow.js

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Machine Learning Rapid Prototyping with IBM Watson Studio

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📚 Coursera Course ↗

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Foundations for Machine Learning

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Fundamentals of CNNs and RNNs

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Advanced Linear Models for Data Science 2: Statistical Linear Models

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AI Materials

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Building Resilient Streaming Systems on GCP em Português Brasileiro

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📚 Coursera Course ↗

Self-paced

Applied Unsupervised Learning in Python

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📚 Coursera Course ↗

Self-paced

Application of AI, InsurTech, and Real Estate Technology

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📚 Coursera Course ↗

Self-paced

Model Thinking

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📚 Coursera Course ↗

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Introduction to AI

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📚 Coursera Course ↗

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Machine Learning with PySpark: Customer Churn Analysis

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📚 Coursera Course ↗

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Natural Language Processing with Probabilistic Models

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Calculus through Data & Modeling: Applying Differentiation

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Julia Programming for Data Science and Machine Learning

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