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  • Explainable Machine Learning Models


Results for "explainable machine learning models"


  • Status: Free Trial
    Free Trial
    S

    Stanford University

    Probabilistic Graphical Models 3: Learning

    Skills you'll gain: Bayesian Network, Applied Machine Learning, Machine Learning Algorithms, Bayesian Statistics, Markov Model, Machine Learning, Statistical Modeling, Probability Distribution, Statistical Methods, Algorithms, Model Evaluation

    4.6
    Rating, 4.6 out of 5 stars
    ·
    304 reviews

    Advanced · Course · 1 - 3 Months

  • Status: Preview
    Preview
    N

    National Taiwan University

    機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations

    Skills you'll gain: Machine Learning Algorithms, Model Evaluation, Classification Algorithms, Algorithms, Supervised Learning, Logistic Regression, Machine Learning, Applied Machine Learning, Data Validation, Regression Analysis, Feature Engineering, Statistical Methods, Verification And Validation

    4.9
    Rating, 4.9 out of 5 stars
    ·
    331 reviews

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of Colorado System

    Machine Learning for Marketers

    Skills you'll gain: Classification And Regression Tree (CART), AI Personalization, Marketing Analytics, Unsupervised Learning, Customer Analysis, Predictive Analytics, Applied Machine Learning, Supervised Learning, Dimensionality Reduction, Marketing Effectiveness, Marketing Strategies, Data-Driven Decision-Making, Strategic Marketing, Campaign Management, Customer experience improvement, Personalized Service, Customer Insights, Model Evaluation, Machine Learning

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    S

    Stanford University

    Probabilistic Graphical Models 2: Inference

    Skills you'll gain: Bayesian Network, Machine Learning Methods, Statistical Inference, Markov Model, Graph Theory, Sampling (Statistics), Applied Machine Learning, Statistical Methods, Probability & Statistics, Algorithms, Probability Distribution, Machine Learning Algorithms, Computational Thinking

    4.6
    Rating, 4.6 out of 5 stars
    ·
    489 reviews

    Advanced · Course · 1 - 3 Months

  • Status: Preview
    Preview
    S

    SAS

    Four Rare Machine Learning Skills All Data Scientists Need

    Skills you'll gain: Predictive Modeling, Predictive Analytics, Marketing Analytics, Statistical Machine Learning, Machine Learning, Data Science, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Model Evaluation, Statistical Analysis

    4.7
    Rating, 4.7 out of 5 stars
    ·
    9 reviews

    Advanced · Course · 1 - 4 Weeks

  • C

    Coursera

    Interpretable Machine Learning Applications: Part 2

    Skills you'll gain: Model Evaluation, Applied Machine Learning, Data Preprocessing, Classification Algorithms, Random Forest Algorithm, Predictive Modeling, Regression Analysis, Decision Tree Learning, Machine Learning, Scikit Learn (Machine Learning Library), Exploratory Data Analysis

    4.2
    Rating, 4.2 out of 5 stars
    ·
    27 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • Status: New
    New
    Status: Free Trial
    Free Trial
    C

    Coursera

    Engineer AI Models: Explain, Tune & Experiment

    Skills you'll gain: Feature Engineering, Fraud detection, Jupyter, Program Management, Responsible AI, Model Evaluation, Project Management, Performance Improvement, Performance Metric, Business Analytics, Credit Risk, Performance Analysis, Performance Tuning, Machine Learning Methods, Test Planning, Test Engineering, Research Design, Technical Communication

    Intermediate · Course · 1 - 4 Weeks

  • Status: New
    New
    Status: Free Trial
    Free Trial
    C

    Coursera

    Automate, Validate, and Promote ML Models Safely

    Skills you'll gain: MLOps (Machine Learning Operations), Responsible AI, CI/CD, Model Deployment, Continuous Integration, Continuous Delivery, Continuous Deployment, Cloud Platforms, Model Evaluation, Automation, Data Governance, Data Pipelines, Performance Tuning, Continuous Monitoring, Performance Analysis

    Intermediate · Course · 1 - 4 Weeks

  • C

    Coursera

    Interpretable Machine Learning Applications: Part 1

    Skills you'll gain: Feature Engineering, Decision Tree Learning, Applied Machine Learning, Model Evaluation, Random Forest Algorithm, Responsible AI, Data Import/Export, Machine Learning, Classification Algorithms

    4.4
    Rating, 4.4 out of 5 stars
    ·
    81 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • Status: Preview
    Preview
    H

    H2O.ai

    Practical Machine Learning on H2O

    Skills you'll gain: Model Evaluation, Unsupervised Learning, Deep Learning, Autoencoders, Anomaly Detection, Applied Machine Learning, Machine Learning, Supervised Learning, Decision Tree Learning, Predictive Modeling, Classification Algorithms, Data Preprocessing, Random Forest Algorithm, Data Manipulation, Feature Engineering, Dimensionality Reduction

    4.5
    Rating, 4.5 out of 5 stars
    ·
    74 reviews

    Intermediate · Course · 1 - 3 Months

  • Status: New
    New
    P

    Packt

    Real-world End to End Machine Learning Ops on Google Cloud

    Skills you'll gain: MLOps (Machine Learning Operations), Google Cloud Platform, Generative AI, Model Deployment, Cloud Management, Kubernetes, Large Language Modeling, CI/CD, Cloud Deployment, Machine Learning Methods, Continuous Deployment, Tensorflow, Model Evaluation, Apache Airflow, AI Workflows, Continuous Integration, Feature Engineering, Containerization, Performance Tuning

    Intermediate · Course · 1 - 3 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    C

    Coursera

    Automate, Analyze, and Evaluate ML Experiments

    Skills you'll gain: MLOps (Machine Learning Operations), Model Evaluation, Key Performance Indicators (KPIs), Business Metrics, Performance Analysis, Performance Measurement, Responsible AI, Test Execution Engine, Performance Metric, Test Automation, Feature Engineering, Verification And Validation, Content Performance Analysis, Machine Learning, Data Ethics, Quality Assessment, Gap Analysis, Cost Benefit Analysis, Research Design, Quantitative Research

    Intermediate · Course · 1 - 4 Weeks

1…678…710

In summary, here are 10 of our most popular explainable machine learning models courses

  • Probabilistic Graphical Models 3: Learning: Stanford University
  • 機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations: National Taiwan University
  • Machine Learning for Marketers: University of Colorado System
  • Probabilistic Graphical Models 2: Inference: Stanford University
  • Four Rare Machine Learning Skills All Data Scientists Need: SAS
  • Interpretable Machine Learning Applications: Part 2: Coursera
  • Engineer AI Models: Explain, Tune & Experiment: Coursera
  • Automate, Validate, and Promote ML Models Safely: Coursera
  • Interpretable Machine Learning Applications: Part 1: Coursera
  • Practical Machine Learning on H2O: H2O.ai

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