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Results for "bias and fairness in machine learning models"
Coursera Project Network
Skills you'll gain: Regression Analysis, NumPy, Applied Machine Learning, Supervised Learning, Machine Learning Algorithms, Machine Learning, Python Programming
Skills you'll gain: Unsupervised Learning, Dimensionality Reduction, Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Feature Engineering, Machine Learning, Statistical Machine Learning, Text Mining, Data Mining, Data Science, Unstructured Data, Big Data, NumPy, Data Analysis, Natural Language Processing, Linear Algebra
- Status: Free
Edge Impulse
Skills you'll gain: Applied Machine Learning, Embedded Systems, Data Processing, Machine Learning, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Data Ethics, Deep Learning, Supervised Learning, Feature Engineering
Imperial College London
Skills you'll gain: Linear Algebra, NumPy, Applied Mathematics, Data Transformation, Data Science, Machine Learning Methods, Algorithms, Data Manipulation, Python Programming
DeepLearning.AI
Skills you'll gain: Computer Vision, Image Analysis, Deep Learning, Artificial Neural Networks, Applied Machine Learning, Artificial Intelligence, Network Architecture, Machine Learning, Data Processing, Algorithms
University of Alberta
Skills you'll gain: Reinforcement Learning, Machine Learning, Artificial Intelligence, Markov Model, Applied Machine Learning, Algorithms
Stanford University
Skills you'll gain: Feature Engineering, Pharmaceuticals, Data Ethics, Clinical Data Management, Health Systems, Healthcare Industry Knowledge, Unstructured Data, Data Mining, Health Care, Applied Machine Learning, Electronic Medical Record, Machine Learning Algorithms, Machine Learning, Data Collection, Health Care Administration, Artificial Neural Networks, Medicare, Quality Improvement, Health Care Procedure and Regulation, Artificial Intelligence
Skills you'll gain: PyTorch (Machine Learning Library), Statistical Methods, Artificial Neural Networks, Deep Learning, Feature Engineering, Probability Distribution, Performance Tuning, Machine Learning, Regression Analysis, Data Processing
DeepLearning.AI
Skills you'll gain: Tensorflow, Computer Vision, Image Analysis, Keras (Neural Network Library), Deep Learning, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning
Skills you'll gain: Generative AI, Reinforcement Learning, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Unsupervised Learning, Artificial Neural Networks, PyTorch (Machine Learning Library), Keras (Neural Network Library), Machine Learning Algorithms, Tensorflow, Computer Vision, Dimensionality Reduction, Network Architecture, Natural Language Processing, NumPy
DeepLearning.AI
Skills you'll gain: Tensorflow, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Performance Tuning, Machine Learning Methods, Artificial Neural Networks, Applied Machine Learning, Machine Learning, Supervised Learning, Machine Learning Algorithms, Network Architecture, Algorithms, Analysis, Debugging
- Status: Free
Sungkyunkwan University
Skills you'll gain: Machine Learning Algorithms, Machine Learning, Python Programming, Supervised Learning, Scikit Learn (Machine Learning Library), Analysis, Regression Analysis, Data Analysis, Classification And Regression Tree (CART), Applied Mathematics, Linear Algebra, Probability
In summary, here are 10 of our most popular bias and fairness in machine learning models courses
- Linear Regression with Python: Coursera Project Network
- Unsupervised Machine Learning: IBM
- Introduction to Embedded Machine Learning: Edge Impulse
- Mathematics for Machine Learning: Linear Algebra: Imperial College London
- Convolutional Neural Networks: DeepLearning.AI
- Fundamentals of Reinforcement Learning: University of Alberta
- AI in Healthcare: Stanford University
- Introduction to Neural Networks and PyTorch: IBM
- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning: DeepLearning.AI
- Deep Learning and Reinforcement Learning: IBM