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  • Regularization Techniques

Results for "regularization techniques"


  • Status: Preview
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    U

    University of Michigan

    The Finite Element Method for Problems in Physics

    Skills you'll gain: Finite Element Methods, Engineering Analysis, Mathematical Software, Numerical Analysis, Mathematical Modeling, Advanced Mathematics, C++ (Programming Language)

    4.6
    Rating, 4.6 out of 5 stars
    ·
    562 reviews

    Intermediate · Course · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Advanced Reproducibility in Cancer Informatics

    Skills you'll gain: Code Review, GitHub, Git (Version Control System), Version Control, Docker (Software), Software Development Tools, Continuous Integration, Containerization, Bioinformatics, Informatics, Automation, Data Analysis, R Programming

    Advanced · Course · 1 - 3 Months

  • Status: New
    New
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    N

    Northeastern University

    Statistical Learning for Engineering Part 1

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Regression Analysis, Applied Machine Learning, Statistical Modeling, Machine Learning Algorithms, PyTorch (Machine Learning Library), Statistical Methods, Statistical Machine Learning, Machine Learning, Predictive Analytics, Predictive Modeling, Machine Learning Software, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Classification Algorithms, Logistic Regression, Unstructured Data, Model Evaluation, Dimensionality Reduction

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Principles of fMRI 1

    Skills you'll gain: Magnetic Resonance Imaging, Medical Imaging, Radiology, Research Design, Image Analysis, Experimentation, Neurology, Data Preprocessing, Science and Research, Statistical Analysis, Statistical Modeling, Statistics

    4.7
    Rating, 4.7 out of 5 stars
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    849 reviews

    Beginner · Course · 1 - 4 Weeks

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    N

    Northeastern University

    Statistical Learning for Engineering Part 2

    Skills you'll gain: PyTorch (Machine Learning Library), Unsupervised Learning, Reinforcement Learning, Supervised Learning, Dimensionality Reduction, Statistical Machine Learning, Machine Learning, Machine Learning Software, Convolutional Neural Networks, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Transfer Learning, Decision Tree Learning, Classification Algorithms, Random Forest Algorithm, Autoencoders, Predictive Modeling, Feature Engineering

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    W

    Wesleyan University

    Regression Modeling in Practice

    Skills you'll gain: Regression Analysis, Logistic Regression, Statistical Methods, Statistical Analysis, Statistical Modeling, Data Analysis, Correlation Analysis, Analysis, SAS (Software), Scatter Plots, Predictive Modeling, Model Evaluation, Data Manipulation, Python Programming

    4.4
    Rating, 4.4 out of 5 stars
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    274 reviews

    Mixed · Course · 1 - 4 Weeks

  • C

    Coursera

    Solving ML Regression Problems with AWS AutoGluon

    Skills you'll gain: AWS SageMaker, Model Evaluation, Applied Machine Learning, Predictive Modeling, Machine Learning, Machine Learning Algorithms, Amazon Web Services, Regression Analysis, Data Analysis

    Beginner · Guided Project · Less Than 2 Hours

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    T

    Technical University of Denmark (DTU)

    Introduction to advanced tomography

    Skills you'll gain: Jupyter, Medical Imaging, 3D Modeling, Image Analysis, Engineering Analysis, Finite Element Methods, Numerical Analysis, Mathematical Modeling, Materials science, Simulation and Simulation Software, Digital Signal Processing, Python Programming, Applied Mathematics

    4.7
    Rating, 4.7 out of 5 stars
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    66 reviews

    Mixed · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Principles of fMRI 2

    Skills you'll gain: Magnetic Resonance Imaging, Data Analysis, Analytical Skills, Image Analysis, Statistical Analysis, Advanced Analytics, Network Analysis, Psychology, Time Series Analysis and Forecasting, Matlab, Neurology, Statistical Modeling, Statistical Methods

    4.7
    Rating, 4.7 out of 5 stars
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    249 reviews

    Mixed · Course · 1 - 4 Weeks

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    N

    Northeastern University

    Machine Learning for Engineers: Algorithms and Applications

    Skills you'll gain: Model Evaluation, Supervised Learning, Statistical Machine Learning, Machine Learning Algorithms, Unsupervised Learning, PyTorch (Machine Learning Library), Applied Machine Learning, Statistical Methods, Statistical Modeling, Machine Learning, Machine Learning Software, Statistical Analysis, Artificial Intelligence and Machine Learning (AI/ML), Regression Analysis, Classification Algorithms, Algorithms, Predictive Modeling, Dimensionality Reduction, Complex Problem Solving

    Intermediate · Course · 1 - 4 Weeks

  • C

    Coursera

    Scikit-Learn For Machine Learning Classification Problems

    Skills you'll gain: Scikit Learn (Machine Learning Library), Classification Algorithms, Applied Machine Learning, Machine Learning Algorithms, Supervised Learning, Random Forest Algorithm, Machine Learning, Unsupervised Learning, Data Analysis

    4.6
    Rating, 4.6 out of 5 stars
    ·
    18 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • Status: Preview
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    N

    National Taiwan University

    機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations

    Skills you'll gain: Supervised Learning, Machine Learning, Statistical Machine Learning, Model Evaluation, Logistic Regression, Theoretical Computer Science, Classification Algorithms, Mathematical Theory & Analysis, Probability & Statistics, Algorithms

    4.9
    Rating, 4.9 out of 5 stars
    ·
    937 reviews

    Beginner · Course · 1 - 3 Months

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In summary, here are 10 of our most popular regularization techniques courses

  • The Finite Element Method for Problems in Physics: University of Michigan
  • Advanced Reproducibility in Cancer Informatics: Johns Hopkins University
  • Statistical Learning for Engineering Part 1: Northeastern University
  • Principles of fMRI 1: Johns Hopkins University
  • Statistical Learning for Engineering Part 2: Northeastern University
  • Regression Modeling in Practice: Wesleyan University
  • Solving ML Regression Problems with AWS AutoGluon: Coursera
  • Introduction to advanced tomography: Technical University of Denmark (DTU)
  • Principles of fMRI 2: Johns Hopkins University
  • Machine Learning for Engineers: Algorithms and Applications: Northeastern University

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