Foundations

ML Fundamentals

Neural networks, backpropagation, gradient descent โ€” the maths behind AI

13170
lessons
Skills in this topic
View full skill map โ†’
ML Maths Basics
beginner
Manipulate vectors and matrices
Supervised Learning
beginner
Train decision trees, random forests, and neural nets
Unsupervised Learning
intermediate
Apply k-means and DBSCAN clustering
ML Pipelines
intermediate
Engineer features and handle missing data
Level: All Beginner Intermediate Advanced
Any Length Short (<5m) Medium (5-20m) Long (>20m)
Newest Popular Oldest
Don't learn data tools without knowing this!
ML Fundamentals
Don't learn data tools without knowing this!
codebasics Beginner 1y ago
Grey Nearing - Flood Casting at Google
ML Fundamentals
Grey Nearing - Flood Casting at Google
Cohere Beginner 1y ago
Validation Set - What's the point? ๐Ÿ“ˆ - Deep Learning Beginner ๐Ÿ” - Topic 153 #ai #ml
ML Fundamentals
Validation Set - What's the point? ๐Ÿ“ˆ - Deep Learning Beginner ๐Ÿ” - Topic 153 #ai #ml
deeplizard Beginner 1y ago
What does a data engineer do?
ML Fundamentals
What does a data engineer do?
codebasics Intermediate 1y ago
Why Oxford: The Executive Diploma Experience
ML Fundamentals
Why Oxford: The Executive Diploma Experience
Saรฏd Business School, University of Oxford Intermediate 1y ago
How Ray Tracing Works - Computerphile
ML Fundamentals
How Ray Tracing Works - Computerphile
Computerphile Beginner 1y ago
Overfitting: Training vs Validation ๐Ÿšซ - Deep Learning Beginner ๐Ÿ“‰ - Topic 152 #ai #ml
ML Fundamentals
Overfitting: Training vs Validation ๐Ÿšซ - Deep Learning Beginner ๐Ÿ“‰ - Topic 152 #ai #ml
deeplizard Beginner 1y ago
The limitless possibilities of on-device ML: From accessible toys to grading grapes
ML Fundamentals
The limitless possibilities of on-device ML: From accessible toys to grading grapes
Google for Developers Beginner 1y ago
Kolmogorov-Arnold Networks: MLP vs KAN, Math, B-Splines, Universal Approximation Theorem
ML Fundamentals
Kolmogorov-Arnold Networks: MLP vs KAN, Math, B-Splines, Universal Approximation Theorem
Umar Jamil Beginner 1y ago
Martin Schmalz appointed first Real Estate Professor & Oxford Future of Real Estate Initiative lead
ML Fundamentals
Martin Schmalz appointed first Real Estate Professor & Oxford Future of Real Estate Initiative lead
Saรฏd Business School, University of Oxford Intermediate 1y ago
I deployed a recommendation model. Testing Models In Production using Interleaving Experiments.
ML Fundamentals
I deployed a recommendation model. Testing Models In Production using Interleaving Experiments.
Underfitted Beginner 1y ago
Generative AI is a Mess ๐Ÿค–
ML Fundamentals
Generative AI is a Mess ๐Ÿค–
Analytics Vidhya Intermediate 1y ago
Data Science Live Stream! Kaggle LMSYS Chatbot Arena Challenge
ML Fundamentals
Data Science Live Stream! Kaggle LMSYS Chatbot Arena Challenge
Rob Mulla Beginner 1y ago
OCW Learning Journeys: Jae-Min's Story #Chemistry #Thermodynamics #Physics #Science #MIT
ML Fundamentals
OCW Learning Journeys: Jae-Min's Story #Chemistry #Thermodynamics #Physics #Science #MIT
MIT OpenCourseWare Beginner 1y ago
AI Quality in Mo's Eyes // Mohamed Elgendy // MLOps Podcast #229 clip
ML Fundamentals
AI Quality in Mo's Eyes // Mohamed Elgendy // MLOps Podcast #229 clip
MLOps.community Intermediate 1y ago
Comment down your favourite term in coding.โœŒ๏ธ
ML Fundamentals
Comment down your favourite term in coding.โœŒ๏ธ
Entri Coding เดฎเดฒเดฏเดพเดณเด‚ Beginner 1y ago
Why We Can Exist | Crash Course Pods: The Universe #2
ML Fundamentals
Why We Can Exist | Crash Course Pods: The Universe #2
CrashCourse Beginner 1y ago
This AI will Change your life!
ML Fundamentals
This AI will Change your life!
Entri Coding เดฎเดฒเดฏเดพเดณเด‚ Beginner 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 17 - Discrete Latent Variable Models
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 17 - Discrete Latent Variable Models
Stanford Online Beginner 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 14 - Energy Based Models
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 14 - Energy Based Models
Stanford Online Beginner 1y ago
The Most Important Integral in Data Science
ML Fundamentals
The Most Important Integral in Data Science
ritvikmath Beginner 1y ago
Validation Set: Unbiased Evaluation โ“ - Ensuring Model Quality ๐ŸŽš๏ธ - Topic 151 #ai #ml
ML Fundamentals
Validation Set: Unbiased Evaluation โ“ - Ensuring Model Quality ๐ŸŽš๏ธ - Topic 151 #ai #ml
deeplizard Beginner 1y ago
Validating for Generalization ๐Ÿ”„ - Beyond Training ๐Ÿ” - Topic 150 #ai #ml
ML Fundamentals
Validating for Generalization ๐Ÿ”„ - Beyond Training ๐Ÿ” - Topic 150 #ai #ml
deeplizard Beginner 1y ago
Sitting Too Much? ๐Ÿ‘ฉโ€๐Ÿ’ป Strengthen Your Glutes ๐Ÿ‘ Cable Pull-Throughs with Mandy
ML Fundamentals
Sitting Too Much? ๐Ÿ‘ฉโ€๐Ÿ’ป Strengthen Your Glutes ๐Ÿ‘ Cable Pull-Throughs with Mandy
deeplizard Beginner 1y ago
Training Set: The Learning Ground ๐Ÿงฌ - Neural Networks 101 ๐Ÿ“˜ - Topic 149 #ai #ml
ML Fundamentals
Training Set: The Learning Ground ๐Ÿงฌ - Neural Networks 101 ๐Ÿ“˜ - Topic 149 #ai #ml
deeplizard Beginner 1y ago
Sanjaya Lall Memorial Panel 2024 - โ€œThe future of work in the age of AIโ€
ML Fundamentals
Sanjaya Lall Memorial Panel 2024 - โ€œThe future of work in the age of AIโ€
Saรฏd Business School, University of Oxford Intermediate 1y ago
Resume Project Challenge 11 #codebasics #data #dataanalyst #datascientist #resumeprojectchallenge
ML Fundamentals
Resume Project Challenge 11 #codebasics #data #dataanalyst #datascientist #resumeprojectchallenge
codebasics Beginner 1y ago
Sanjaya Lall Memorial Lecture 2024
ML Fundamentals
Sanjaya Lall Memorial Lecture 2024
Saรฏd Business School, University of Oxford Intermediate 1y ago
Splitting Data: 3 Key Sets ๐Ÿ”ข - Intro to Sets in ML ๐Ÿ“ - Topic 148 #ai #ml
ML Fundamentals
Splitting Data: 3 Key Sets ๐Ÿ”ข - Intro to Sets in ML ๐Ÿ“ - Topic 148 #ai #ml
deeplizard Beginner 1y ago
Focus on Fundamentals, not just the tools!
ML Fundamentals
Focus on Fundamentals, not just the tools!
codebasics Intermediate 1y ago
Training, Validation, Testing ๐Ÿ“Š - Neural Network Basics ๐Ÿ“˜ - Topic 147 #ai #ml
ML Fundamentals
Training, Validation, Testing ๐Ÿ“Š - Neural Network Basics ๐Ÿ“˜ - Topic 147 #ai #ml
deeplizard Beginner 1y ago
Focus on these timeless skills #codebasics #dataanalyst #data #datascientist #skills
ML Fundamentals
Focus on these timeless skills #codebasics #dataanalyst #data #datascientist #skills
codebasics Beginner 1y ago
How can we correct the warped mirror that AI holds up to nature?
ML Fundamentals
How can we correct the warped mirror that AI holds up to nature?
Saรฏd Business School, University of Oxford Intermediate 1y ago
Total Network Learnable Parameters ๐Ÿ“ˆ - Beginner Example ๐Ÿงฎ - Topic 146 #ai #ml
ML Fundamentals
Total Network Learnable Parameters ๐Ÿ“ˆ - Beginner Example ๐Ÿงฎ - Topic 146 #ai #ml
deeplizard Beginner 1y ago
AI Engineering Roadmap!
ML Fundamentals
AI Engineering Roadmap!
codebasics Intermediate 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 13 - Score Based Models
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 13 - Score Based Models
Stanford Online Beginner 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 12 - Energy Based Models
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 12 - Energy Based Models
Stanford Online Beginner 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 11 - Energy Based Models
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 11 - Energy Based Models
Stanford Online Beginner 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 10 - GANs
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 10 - GANs
Stanford Online Beginner 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 9 - GANs
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 9 - GANs
Stanford Online Beginner 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 8 - Normalizing Flows
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 8 - Normalizing Flows
Stanford Online Beginner 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 7 - Normalizing Flows
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 7 - Normalizing Flows
Stanford Online Beginner 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 6 - VAEs
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 6 - VAEs
Stanford Online Beginner 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 5 - VAEs
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 5 - VAEs
Stanford Online Beginner 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 4 - Maximum Likelihood Learning
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 4 - Maximum Likelihood Learning
Stanford Online Beginner 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 3 - Autoregressive Models
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 3 - Autoregressive Models
Stanford Online Beginner 1y ago
Stanford CS236: Deep Generative Models I 2023 I Lecture 2 - Background
ML Fundamentals
Stanford CS236: Deep Generative Models I 2023 I Lecture 2 - Background
Stanford Online Beginner 1y ago
Summing Network Params ๐Ÿงฎ - Beginners' Guide to Totaling ๐Ÿ” - Topic 145 #ai #ml
ML Fundamentals
Summing Network Params ๐Ÿงฎ - Beginners' Guide to Totaling ๐Ÿ” - Topic 145 #ai #ml
deeplizard Beginner 1y ago
๐Ÿ“š Coursera Courses Opens on Coursera ยท Free to audit
1 / 3 View all โ†’
AI Workflow: AI in Production
๐Ÿ“š Coursera Course โ†—
Self-paced
AI Workflow: AI in Production
Opens on Coursera โ†—
Introduction to Linear Algebra
๐Ÿ“š Coursera Course โ†—
Self-paced
Introduction to Linear Algebra
Opens on Coursera โ†—
Responsible AI: Applying AI Principles with Google Cloud - Deutsch
๐Ÿ“š Coursera Course โ†—
Self-paced
Responsible AI: Applying AI Principles with Google Cloud - Deutsch
Opens on Coursera โ†—
Le Derivate: come si calcolano
๐Ÿ“š Coursera Course โ†—
Self-paced
Le Derivate: come si calcolano
Opens on Coursera โ†—
AWS: Model Training , Optimization & Deployment
๐Ÿ“š Coursera Course โ†—
Self-paced
AWS: Model Training , Optimization & Deployment
Opens on Coursera โ†—
Excel: Apply & Evaluate Unsupervised Clustering
๐Ÿ“š Coursera Course โ†—
Self-paced
Excel: Apply & Evaluate Unsupervised Clustering
Opens on Coursera โ†—