📐 ML Fundamentals

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

ML Fundamentals

Stanford CS229 I Societal impact of ML (Guest lecture by Prof. James Zou) I 2022 I Lecture 18

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Analysis Methods for NLU, Part 3: Feature Attribution I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

BentoML Tutorial: Build Production Grade AI Applications

Krish Naik Intermediate 2y ago

ML Fundamentals

Position Encodings (Natural Language Processing at UT Austin)

Greg Durrett Intermediate 2y ago

ML Fundamentals

The Tripolar Nuclear Paradox

ritvikmath Intermediate 2y ago

ML Fundamentals

Leveraging ML Inference for Generative AI on AWS - AWS ML Heroes in 15

AWS Developers Intermediate 2y ago

ML Fundamentals

Why is the Formula for F1-Score Unnecessarily Complicated?

ritvikmath Intermediate 2y ago

ML Fundamentals

Income Prediction Machine Learning Project in Python

NeuralNine Intermediate 2y ago

ML Fundamentals

Analytics in 15: What's New with Amazon EMR

AWS Developers Intermediate 2y ago

ML Fundamentals

88% of employers agree that a Professional Certificate strengthens a candidate’s application.

Coursera Intermediate 2y ago

ML Fundamentals

19. Cholesterol Biosynthesis 1

MIT OpenCourseWare Intermediate 2y ago

ML Fundamentals

Build your own Stable Doodle: Sketch to Image

Abhishek Thakur Intermediate 2y ago

ML Fundamentals

#35 AI for Good Specialization [Course 1, Week 3, Lesson 1]

DeepLearningAI Intermediate 2y ago

ML Fundamentals

#34 AI for Good Specialization [Course 1, Week 3, Lesson 1]

DeepLearningAI Intermediate 2y ago

ML Fundamentals

Course: Machine Learning for Business Decision Optimization

Weights & Biases Intermediate 2y ago

ML Fundamentals

Greg Michaelson AutoML as it should have always been | JupyterCon 2023

JupyterCon Intermediate 2y ago

ML Fundamentals

Emilien Schultz How to convince French HSS researchers to use Jupyter Notebooks ?

JupyterCon Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Analysis Methods for NLU, Part 2: Probing I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Behavioral Eval of NLU Models, Pt 7: DynaSent and Conclusion I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U I Behavioral Eval of NLU Models, Pt 2: Analytical Considerations I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: Natural Language Understanding I Homework 3 I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Information Retrieval, Part 5: Datasets and Conclusion I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Information Retrieval, Part 4: Neural IR I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Information Retrieval, Part 2: Classical IR I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Information Retrieval, Part 1: Guiding Ideas I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: Natural Language Understanding I Homework 2 I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Contextual Word Representations, Part 10: Wrap-up I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Contextual Word Representations, Part 9: Distillation I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Contextual Word Representations, Part 7: ELECTRA I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Contextual Word Representations, Part 6: RoBERTa I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Contextual Word Representations, Part 5: BERT I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Contextual Word Representations, Part 3: Positional Encoding I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford XCS224U: NLU I Contextual Word Representations, Part 1: Guiding Ideas I Spring 2023

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford CS229 I Basic concepts in RL, Value iteration, Policy iteration I 2022 I Lecture 17

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford CS229 I K-Means, GMM (non EM), Expectation Maximization I 2022 I Lecture 12

Stanford Online Intermediate 2y ago

ML Fundamentals

Stanford CS229 I Weighted Least Squares, Logistic regression, Newton's Method I 2022 I Lecture 3

Stanford Online Intermediate 2y ago

ML Fundamentals

I couldn't solve this puzzle, so I built an AI that could

ritvikmath Intermediate 2y ago

ML Fundamentals

Gerencia de Producto: Qué es y por qué es importante en Latino América

Stanford Online Intermediate 2y ago

ML Fundamentals

Deliver high-performance ML models faster with MLOps tools

AWS Developers Intermediate 2y ago

ML Fundamentals

#33 AI for Good Specialization [Course 1, Week 3, Lesson 1]

DeepLearningAI Intermediate 2y ago

ML Fundamentals

#32 AI for Good Specialization [Course 1, Week 3, Lesson 1]

DeepLearningAI Intermediate 2y ago

ML Fundamentals

#31 AI for Good Specialization [Course 1, Week 3, Lesson 1]

DeepLearningAI Intermediate 2y ago

ML Fundamentals

#30 AI for Good Specialization [Course 1, Week 3, Lesson 1]

DeepLearningAI Intermediate 2y ago

ML Fundamentals

#29 AI for Good Specialization [Course 1, Week 3, Lesson 1]

DeepLearningAI Intermediate 2y ago

ML Fundamentals

#18 AI for Good Specialization [Course 1, Week 2, Lesson 1]

DeepLearningAI Intermediate 2y ago

ML Fundamentals

#16 AI for Good Specialization [Course 1, Week 2, Lesson 1]

DeepLearningAI Intermediate 2y ago

ML Fundamentals

Giuseppe Angelo Porcelli Boost productivity with generative AI and scalable development using Jupy

JupyterCon Intermediate 2y ago

ML Fundamentals

Thomas Dupriez Flexible course management and validation system using Jupyterhub with additional s

JupyterCon Intermediate 2y ago

📚 Coursera Courses Opens on Coursera · Free to audit

← 1 / 3 → View all →

📚 Coursera Course ↗

Self-paced

Graduate Admission Prediction with Pyspark ML

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

AI for Design and Optimization

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Deep Learning - Recurrent Neural Networks with TensorFlow

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Securing AI Systems

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Machine Learning and AI Applications in Healthcare

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Digital Signal Processing 1: Basic Concepts and Algorithms

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Practical Machine Learning

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

ML Parameters Optimization: GridSearch, Bayesian, Random

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Analyze and Build Deep Learning Models with TensorFlow

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Introdução à IA e ao machine learning no Google Cloud

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Apply SOLID Design to Optimize Java ML

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Getting Started with Machine Learning at the Edge on Arm

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Automate, Evaluate and Deploy ML Models Confidently

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Introduction to Open Source Application Development

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Calculus through Data & Modelling: Techniques of Integration

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

AI Applications in Marketing and Finance

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Probabilistic Graphical Models 2: Inference

Opens on Coursera ↗

📚 Coursera Course ↗

Self-paced

Statistical Learning for Engineering Part 2

Opens on Coursera ↗