Filter by
The language used throughout the course, in both instruction and assessments.
Results for "regularization+techniques"
University of Washington
Skills you'll gain: Algorithms, Applied Machine Learning, Human Learning, Machine Learning, Machine Learning Algorithms, Data Analysis, Machine Learning Software, Statistical Machine Learning, Python Programming, Computer Programming, Exploratory Data Analysis, Regression
Imperial College London
Skills you'll gain: Algebra, Linear Algebra, Machine Learning, Mathematics, Computer Programming, Dimensionality Reduction, Mathematical Theory & Analysis, Python Programming, Statistical Analysis, Applied Mathematics
DeepLearning.AI
Skills you'll gain: Machine Learning, Calculus, Differential Equations, Mathematics, Machine Learning Algorithms, Regression, Algebra, Algorithms, Artificial Neural Networks
Johns Hopkins University
- Status: Free
Coursera Project Network
Skills you'll gain: Data Science, Deep Learning, Machine Learning, Python Programming, Regression
Coursera Project Network
Skills you'll gain: Regression
- Status: Free
National Taiwan University
Skills you'll gain: Mathematics, Human Learning, Machine Learning
- Status: Free
University of Minnesota
Skills you'll gain: Algebra, Linear Algebra, Mathematics, Problem Solving
Coursera Project Network
Skills you'll gain: Deep Learning
Johns Hopkins University
Skills you'll gain: General Statistics, Probability & Statistics, Linear Algebra, Mathematics, Regression, Algebra, Correlation And Dependence
In summary, here are 10 of our most popular regularization+techniques courses
- Machine Learning Foundations: A Case Study Approach:Â University of Washington
- Mathematics for Machine Learning: PCA:Â Imperial College London
- Calculus for Machine Learning and Data Science:Â DeepLearning.AI
- Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors:Â Johns Hopkins University
- Regression & Forecasting for Data Scientists using Python:Â EDUCBA
- Linear Regression with Python:Â Coursera Project Network
- Image Segmentation, Filtering, and Region Analysis:Â MathWorks
- Essential Causal Inference Techniques for Data Science:Â Coursera Project Network
- 機器å¸ç¿’基石下 (Machine Learning Foundations)---Algorithmic Foundations: National Taiwan University
- Matrix Methods:Â University of Minnesota