Foundations

ML Fundamentals

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

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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
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Humans in the Loop are a defining factor in Machine Learning
ML Fundamentals
Humans in the Loop are a defining factor in Machine Learning
MLOps.community Intermediate 6y ago
Advanced Theory | Neural Style Transfer #4
ML Fundamentals
Advanced Theory | Neural Style Transfer #4
Aleksa Gordić - The AI Epiphany Advanced 6y ago
Tips N Tricks #6: How to train multiple deep neural networks on TPUs simultaneously
ML Fundamentals
Tips N Tricks #6: How to train multiple deep neural networks on TPUs simultaneously
Abhishek Thakur Intermediate 6y ago
Sharing My Experience- How Teaching Has Improved My Data Science Skills
ML Fundamentals
Sharing My Experience- How Teaching Has Improved My Data Science Skills
Krish Naik Intermediate 6y ago
Pytorch TensorBoard Tutorial
ML Fundamentals ⚡ AI Lesson
Pytorch TensorBoard Tutorial
Aladdin Persson Beginner 6y ago
Neural Networks from Scratch - P.2 Coding a Layer
ML Fundamentals ⚡ AI Lesson
Neural Networks from Scratch - P.2 Coding a Layer
sentdex Beginner 6y ago
Current State Of Machine Learning
ML Fundamentals
Current State Of Machine Learning
MLOps.community Beginner 6y ago
Neural Networks Simply Explained (Theory)
ML Fundamentals ⚡ AI Lesson
Neural Networks Simply Explained (Theory)
NeuralNine Beginner 6y ago
Scala Tutorial: A scalable language
ML Fundamentals ⚡ AI Lesson
Scala Tutorial: A scalable language
DataCamp Beginner 6y ago
R Tutorial: Machine Learning with Tree-Based Models | Intro
ML Fundamentals
R Tutorial: Machine Learning with Tree-Based Models | Intro
DataCamp Beginner 6y ago
Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
ML Fundamentals ⚡ AI Lesson
Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
Weights & Biases Beginner 6y ago
Sequence Models with Pujaa Rajan
ML Fundamentals ⚡ AI Lesson
Sequence Models with Pujaa Rajan
Weights & Biases Beginner 6y ago
How to Transition into Data Science: from Computer Science to Data Science
ML Fundamentals
How to Transition into Data Science: from Computer Science to Data Science
365 Data Science Beginner 6y ago
Inside TensorFlow: TF Debugging
ML Fundamentals ⚡ AI Lesson
Inside TensorFlow: TF Debugging
TensorFlow Intermediate 6y ago
Intro to Deep Learning -- L15 Autoencoders [Stat453, SS20]
ML Fundamentals
Intro to Deep Learning -- L15 Autoencoders [Stat453, SS20]
Sebastian Raschka Beginner 6y ago
Decoding Logistic Regression - A Simple and Comprehensive Explanation
ML Fundamentals
Decoding Logistic Regression - A Simple and Comprehensive Explanation
What's AI by Louis-François Bouchard Beginner 6y ago
How deep learning can detect cancerous tissue (AI For Medicine)
ML Fundamentals
How deep learning can detect cancerous tissue (AI For Medicine)
DeepLearningAI Beginner 6y ago
Automate LifeCycle Of Data Science Projects By Using This Open Source Library
ML Fundamentals
Automate LifeCycle Of Data Science Projects By Using This Open Source Library
Krish Naik Beginner 6y ago
Intro to Deep Learning -- L14 Intro to Recurrent Neural Networks [Stat453, SS20]
ML Fundamentals ⚡ AI Lesson
Intro to Deep Learning -- L14 Intro to Recurrent Neural Networks [Stat453, SS20]
Sebastian Raschka Beginner 6y ago
Pytorch ResNet implementation from Scratch
ML Fundamentals
Pytorch ResNet implementation from Scratch
Aladdin Persson Beginner 6y ago
PyTorch Tutorial 17 - Saving and Loading Models
ML Fundamentals ⚡ AI Lesson
PyTorch Tutorial 17 - Saving and Loading Models
Patrick Loeber Beginner 6y ago
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
ML Fundamentals
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Yannic Kilcher Advanced 6y ago
Demystifying Data Mining - A Clear and Concise Explanation
ML Fundamentals
Demystifying Data Mining - A Clear and Concise Explanation
What's AI by Louis-François Bouchard Beginner 6y ago
This Neural Network Learned To Look Around In Real Scenes! (NERF)
ML Fundamentals
This Neural Network Learned To Look Around In Real Scenes! (NERF)
Two Minute Papers Beginner 6y ago
Specific challenges in Machine Learning
ML Fundamentals
Specific challenges in Machine Learning
MLOps.community Intermediate 6y ago
MLOps: Airflow Pros and Cons
ML Fundamentals
MLOps: Airflow Pros and Cons
MLOps.community Intermediate 6y ago
R Tutorial: Evaluating classification model performance
ML Fundamentals
R Tutorial: Evaluating classification model performance
DataCamp Beginner 6y ago
R Tutorial: Introduction to classification trees
ML Fundamentals
R Tutorial: Introduction to classification trees
DataCamp Beginner 6y ago
MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
ML Fundamentals
MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
MLOps.community Intermediate 6y ago
MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
ML Fundamentals
MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
MLOps.community Beginner 6y ago
Theory: Applications of Data Science
ML Fundamentals ⚡ AI Lesson
Theory: Applications of Data Science
DataCamp Intermediate 6y ago
R Tutorial: Nonlinear Modeling in R with GAMs | Intro
ML Fundamentals
R Tutorial: Nonlinear Modeling in R with GAMs | Intro
DataCamp Beginner 6y ago
Teachable Machine By Google- Train Your Model With Ease
ML Fundamentals
Teachable Machine By Google- Train Your Model With Ease
Krish Naik Beginner 6y ago
Python Tutorial: Introducing convolutional neural networks
ML Fundamentals ⚡ AI Lesson
Python Tutorial: Introducing convolutional neural networks
DataCamp Beginner 6y ago
Python Tutorial: Image classification with Keras
ML Fundamentals
Python Tutorial: Image classification with Keras
DataCamp Beginner 6y ago
Python Tutorial: Classifying images
ML Fundamentals
Python Tutorial: Classifying images
DataCamp Beginner 6y ago
Python Tutorial : Data transforms, features, and targets
ML Fundamentals ⚡ AI Lesson
Python Tutorial : Data transforms, features, and targets
DataCamp Beginner 6y ago
Python Tutorial : Linear modeling with financial data
ML Fundamentals ⚡ AI Lesson
Python Tutorial : Linear modeling with financial data
DataCamp Beginner 6y ago
Python Tutorial : Machine Learning for Finance in Python
ML Fundamentals
Python Tutorial : Machine Learning for Finance in Python
DataCamp Beginner 6y ago
How to Build A Data Science Portfolio That Can Get You Jobs?
ML Fundamentals
How to Build A Data Science Portfolio That Can Get You Jobs?
Krish Naik Intermediate 6y ago
R Tutorial : Network analysis in R: A tidy approach
ML Fundamentals
R Tutorial : Network analysis in R: A tidy approach
DataCamp Beginner 6y ago
PyTorch Tutorial : Backpropagation by auto-differentiation
ML Fundamentals
PyTorch Tutorial : Backpropagation by auto-differentiation
DataCamp Beginner 6y ago
PyTorch Tutorial : Introduction to PyTorch
ML Fundamentals
PyTorch Tutorial : Introduction to PyTorch
DataCamp Beginner 6y ago
PyTorch Tutorial : Introduction to Neural Networks
ML Fundamentals
PyTorch Tutorial : Introduction to Neural Networks
DataCamp Beginner 6y ago
PyTorch Tutorial : Forward propagation
ML Fundamentals ⚡ AI Lesson
PyTorch Tutorial : Forward propagation
DataCamp Beginner 6y ago
Python Tutorial : Writing Efficient Python Code
ML Fundamentals
Python Tutorial : Writing Efficient Python Code
DataCamp Beginner 6y ago
Python Tutorial : Introducing XGBoost
ML Fundamentals
Python Tutorial : Introducing XGBoost
DataCamp Beginner 6y ago
Live Q&A Data Science
ML Fundamentals
Live Q&A Data Science
Krish Naik Intermediate 6y ago
📚 Coursera Courses Opens on Coursera · Free to audit
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Compare Models with Experiments in Azure ML Studio
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Self-paced
Compare Models with Experiments in Azure ML Studio
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Statistics and Calculus Methods for Data Analysis
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Self-paced
Statistics and Calculus Methods for Data Analysis
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Naive Bayes 101: Resume Selection with Machine Learning
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Self-paced
Naive Bayes 101: Resume Selection with Machine Learning
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Fundamentals of Machine Learning
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Fundamentals of Machine Learning
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Physics 102 - Electric Potential and DC Circuits
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Self-paced
Physics 102 - Electric Potential and DC Circuits
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Optimize Vision Datasets: Augment and Analyze
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Self-paced
Optimize Vision Datasets: Augment and Analyze
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