📐 ML Fundamentals

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

ML Fundamentals

Build a Deep Facial Recognition App // Part 6 - Making Facial Recognition Predictions // #Python

Nicholas Renotte Beginner 4y ago

ML Fundamentals

How mlctl Helps Intuit's Workflow // Srivathsan Canchi // Coffee Sessions # 50 short clip

MLOps.community Beginner 4y ago

ML Fundamentals

Newton’s fractal (which Newton knew nothing about)

3Blue1Brown Beginner 4y ago

ML Fundamentals

Create feature interactions using PolynomialFeatures

Data School Beginner 4y ago

ML Fundamentals

Data Selection for Data-Centric AI: Data Quality Over Quantity // Cody Coleman //Coffee Sessions#59

MLOps.community Beginner 4y ago

ML Fundamentals

PyTorch Book Reading - 7. Training a Tumor classifier

Weights & Biases Beginner 4y ago

ML Fundamentals

How Al is Changing Marketing and SEO (And How to Use it In Your Business)

Neil Patel Beginner 4y ago

ML Fundamentals

Machine Learning Tutorial Python - 19: Principal Component Analysis (PCA) with Python Code

codebasics Beginner 4y ago

ML Fundamentals

UIUC's Online Master's in Computer Science MCS & MCS-DS Degrees Webinar

Coursera Beginner 4y ago

ML Fundamentals

Speed up GridSearchCV using parallel processing

Data School Beginner 4y ago

ML Fundamentals

Grokking: Generalization beyond Overfitting on small algorithmic datasets (Paper Explained)

Yannic Kilcher Beginner 4y ago

ML Fundamentals

1. Introduction and Matrix Multiplication

MIT OpenCourseWare Beginner 4y ago

ML Fundamentals

Introducing the first Women in Machine Learning Symposium

TensorFlow Beginner 4y ago

ML Fundamentals

Rebecca Fiebrink - Creative AI Conversations

Runway Beginner 4y ago

ML Fundamentals

TensorFlow for Computer Vision Course - Full Python Tutorial for Beginners

freeCodeCamp.org Beginner 4y ago

ML Fundamentals

PyTorch Book Reading - 6. Working CT Scan Data in PyTorch, Classifying Tumours.

Weights & Biases Beginner 4y ago

ML Fundamentals

Perform Easy EDA And Generate Python Using Mito

Krish Naik Beginner 4y ago

ML Fundamentals

Machine Learning Model Deployment with Python (Streamlit + MLflow) | Part 2/2

DeepFindr Beginner 4y ago

ML Fundamentals

What is Hypothesis Testing ? Math, Statistics for data science, machine learning

codebasics Beginner 4y ago

ML Fundamentals

TensorFlow.js Community "Show & Tell" #6

TensorFlow Beginner 4y ago

ML Fundamentals

Build a Deep Facial Recognition App // Part 5 - Training a Siamese Neural Network // #Python

Nicholas Renotte Beginner 4y ago

ML Fundamentals

Machine Learning Model Deployment with Python (Streamlit + MLflow) | Part 1/2

DeepFindr Beginner 4y ago

ML Fundamentals

Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence | Lex Fridman Podcast #225

Lex Fridman Beginner 4y ago

ML Fundamentals

Talks S2E7 (Konrad Banachewicz): Time Series Analysis - Vintage Toolkit For Modern Times

Abhishek Thakur Beginner 4y ago

ML Fundamentals

Time Series Forecasting Made Easy Using Dart Library - Perform Multivariate Forecasting In No Time

Krish Naik Beginner 4y ago

ML Fundamentals

Gies College of Business Webinar: From MOOCs to Master's

Coursera Beginner 4y ago

ML Fundamentals

#71 Scaling Machine Learning Adoption: A Pragmatic Approach (with Noah Gift)

DataCamp Beginner 4y ago

ML Fundamentals

Jay McClelland: Neural Networks and the Emergence of Cognition | Lex Fridman Podcast #222

Lex Fridman Beginner 4y ago

ML Fundamentals

Introduction to Generative Adversarial Networks (Tutorial Recording at ISSDL 2021)

Sebastian Raschka Beginner 4y ago

ML Fundamentals

Talks S2E6 (Louise Ferbach): Deep Learning For Survival Analysis

Abhishek Thakur Beginner 4y ago

ML Fundamentals

10 Types of Features your Location ML Model is Missing // Anne Cocos // Coffee Sessions #58

MLOps.community Beginner 4y ago

ML Fundamentals

Use OrdinalEncoder instead of OneHotEncoder with tree-based models

Data School Beginner 4y ago

ML Fundamentals

Three Categories of SGT // Alex Chung // Coffee #50 short clip

MLOps.community Beginner 4y ago

ML Fundamentals

Chai Time Kaggle Talks with Andrada Olteanu - EDA Grandmastery

Weights & Biases Beginner 4y ago

ML Fundamentals

Detecting model misbehavior with W&B

Weights & Biases Beginner 4y ago

ML Fundamentals

Drop the first category from binary features (only) with OneHotEncoder

Data School Beginner 4y ago

ML Fundamentals

Managing Outcomes Generated by Data Scientists // Stefan Krawczyk // Coffee Sessions #49 short clip

MLOps.community Beginner 4y ago

ML Fundamentals

Importance of Platform // Julien Chaumond // Coffee Sessions #48 short clip

MLOps.community Beginner 4y ago

ML Fundamentals

Deep learning project end to end | Potato Disease Classification - 8 : Mobile App in React Native

codebasics Beginner 4y ago

ML Fundamentals

Engineering Best Practices for Machine Learning // Alex Serban // MLOps Meetup #79

MLOps.community Beginner 4y ago

ML Fundamentals

Estimators only print parameters that have been changed

Data School Beginner 4y ago

ML Fundamentals

Chris Albon — ML Models and Infrastructure at Wikimedia

Weights & Biases Beginner 4y ago

ML Fundamentals

W&B Fastbook Reading Group — 15. Application Architectures Deep Dive

Weights & Biases Beginner 4y ago

ML Fundamentals

Build a Deep Facial Recognition App // Part 4 - Building a Siamese Neural Network // #Python

Nicholas Renotte Beginner 4y ago

ML Fundamentals

Load a toy dataset into a DataFrame

Data School Beginner 4y ago

ML Fundamentals

PyTorch Book Reading - 4. Train your first CNN using Torch

Weights & Biases Beginner 4y ago

ML Fundamentals

Career Gap and Now a Machine Learning Engineer At Sony

codebasics Beginner 4y ago

ML Fundamentals

Machine Learning Projects You NEVER Knew Existed

Nicholas Renotte Beginner 4y ago

📚 Coursera Courses Opens on Coursera · Free to audit

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Self-paced

Responsible AI: Applying AI Principles with Google Cloud

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Machine Learning: Random Forest with Python from Scratch©

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📚 Coursera Course ↗

Self-paced

Machine Learning and NLP Basics

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📚 Coursera Course ↗

Self-paced

CUDA at Scale for the Enterprise

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📚 Coursera Course ↗

Self-paced

Introduction to Neural Networks and PyTorch

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📚 Coursera Course ↗

Self-paced

Introduction to AI and Machine Learning

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📚 Coursera Course ↗

Self-paced

Analyze Data Science Concepts Using R

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📚 Coursera Course ↗

Self-paced

Optimize Vision Datasets: Augment and Analyze

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📚 Coursera Course ↗

Self-paced

Introduction to Neural Networks

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📚 Coursera Course ↗

Self-paced

Practical Machine Learning on H2O

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📚 Coursera Course ↗

Self-paced

Divide and Conquer, Sorting and Searching, and Randomized Algorithms

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📚 Coursera Course ↗

Self-paced

The Data Science Profession – Student View

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📚 Coursera Course ↗

Self-paced

Probabilistic Graphical Models 1: Representation

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📚 Coursera Course ↗

Self-paced

AWS Glue Getting Started

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📚 Coursera Course ↗

Self-paced

Introduction to Long Short Term Memory (LSTM) Training

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📚 Coursera Course ↗

Self-paced

Machine Learning for Healthcare Professionals

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📚 Coursera Course ↗

Self-paced

Trees, SVM and Unsupervised Learning

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📚 Coursera Course ↗

Self-paced

Linear Algebra and Regression Fundamentals for Data Science

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