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Train Machine Learning Models
  • Level Professional
  • المدة 29 ساعات hours
  • الطبع بواسطة CertNexus
  • Offered by

عن

This course is designed for business professionals that wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that solve classification, regression and forecasting, and clustering problems. To be successful in this course a learner should have a background in computing technology, including some aptitude in computer programming.

الوحدات

Overview

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Course Intro: Train Machine Learning Models

2 Readings

  • Overview
  • Get help and meet other learners. Join your Community!

Machine Learning Concepts

    • 9 Videos
    • 2 Readings
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Identifying Machine Learning Concepts

9 Videos

  • Machine Learning
  • Machine Learning Algorithms
  • Algorithm Selection
  • Iterative Tuning
  • Bias and Variance
  • Model Generalization
  • The Bias–Variance Tradeoff
  • Holdout Method
  • Parameters

2 Readings

  • Cross-Validation
  • Guidelines for Training Machine Learning Models

Test a Hypothesis

    • 5 Videos
    • 2 Readings
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Testing a Hypothesis

5 Videos

  • Hypothesis and DOE
  • Hypothesis Testing
  • A/B Tests
  • p-value
  • Confidence Interval

2 Readings

  • Additional Hypothesis Testing Methods
  • Guidelines for Testing a Hypothesis

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Preparing to Train a Machine Learning Model

1 Discussions

  • Reflect on What You've Learned

Overview

    • 1 Readings
Show info about module content

1 Readings

  • Overview

Train Logistic Regression Models

    • 2 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Training a Logistic Regression Model

2 Videos

  • Logistic Regression
  • Multinomial Logistic Regression

1 Readings

  • Guidelines for Training Logistic Regression Models

Train k-Nearest Neighbor Models

    • 1 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Training a k-NN Model

1 Videos

  • k-Nearest Neighbor (k-NN)

1 Readings

  • Guidelines for Training k-NN Models

Train Support-Vector Machine Models for Classification

    • 1 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Training an SVM Classification Model

1 Videos

  • Support-Vector Machines (SVMs)

1 Readings

  • Guidelines for Training SVM Classification Models

Train Naïve Bayes Models

    • 1 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Training a Naïve Bayes Model

1 Videos

  • Naïve Bayes

1 Readings

  • Guidelines for Training Naïve Bayes Models

Train Tree-Based Models for Classification

    • 5 Videos
    • 2 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Training Classification Decision Trees and Ensemble Models

5 Videos

  • Decision Tree
  • Customer Retention Example Tree
  • Pruning
  • Ensemble Learning and Random Forests
  • Gradient Boosting

2 Readings

  • CART Hyperparameters
  • Guidelines for Training Classification Decision Trees and Ensemble Models

Tune Classification Models

    • 1 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Tuning Classification Models

1 Videos

  • Hyperparameter Optimization

1 Readings

  • Guidelines for Tuning Classification Models

Evaluate Classification Models

    • 7 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Evaluating Classification Models

7 Videos

  • Evaluation Metrics
  • Classification Model Performance
  • Confusion Matrix
  • Accuracy, Precision, Recall, and Specificity
  • Precision–Recall Tradeoff and F₁ Score
  • Receiver Operating Characteristic (ROC) Curve
  • Learning Curve

1 Readings

  • Guidelines for Evaluating Classification Models

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Developing Classification Models

1 Discussions

  • Reflect on What You've Learned

Overview

    • 1 Readings
Show info about module content

1 Readings

  • Overview

Train Linear Regression Models

    • 4 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Training a Linear Regression Model

4 Videos

  • Linear Regression
  • Linear Regression in Machine Learning
  • Matrices in Linear Regression
  • Normal Equation

1 Readings

  • Guidelines for Training Linear Regression Models

Train Tree-Based Models for Regression

    • 1 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Training Regression Trees and Ensemble Models

1 Videos

  • Regression Using Decision Trees and Ensemble Models

1 Readings

  • Guidelines for Training Regression Trees and Ensemble Models

Train Forecasting Models

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • Forecasting
  • Autoregressive Integrated Moving Average (ARIMA)

1 Readings

  • Guidelines for Training Forecasting Models

Tune Regression Models

    • 4 Videos
    • 2 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Tuning Regression Models

4 Videos

  • Cost Function
  • Regularization
  • Gradient Descent
  • Grid/Randomized Search for Regression

2 Readings

  • Regularization Techniques
  • Guidelines for Tuning Regression Models

Evaluate Regression Models

    • 2 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Evaluating Regression Models

2 Videos

  • Mean Squared Error (MSE) and Mean Absolute Error (MAE)
  • Coefficient of Determination

1 Readings

  • Guidelines for Evaluating Regression Models

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Developing Regression Models

1 Discussions

  • Reflect on What You've Learned

Overview

    • 1 Readings
Show info about module content

1 Readings

  • Overview

Train k-Means Clustering Models

    • 1 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Training a k-Means Clustering Model

1 Videos

  • k-Means Clustering

1 Readings

  • Guidelines for Training k-Means Clustering Models

Train Hierarchical Clustering Models

    • 1 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Training a Hierarchical Clustering Model

1 Videos

  • Hierarchical Clustering

1 Readings

  • Guidelines for Training Hierarchical Clustering Models

Tune Clustering Models

    • 2 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Tuning Clustering Models

2 Videos

  • Latent Class Analysis
  • Clustering Hyperparameters and Tuning

1 Readings

  • Guidelines for Tuning Clustering Models

Evaluate Clustering Models

    • 5 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Evaluating Clustering Models

5 Videos

  • Evaluation Metrics for Clustering
  • Elbow Point
  • Cluster Sum of Squares
  • Silhouette Analysis
  • When to Stop Hierarchical Clustering

1 Readings

  • Guidelines for Evaluating Clustering Models

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Developing Clustering Models

1 Discussions

  • Reflect on What You've Learned

Project

    • 1 PeerReview
    • 1 Lab
Show info about module content

1 Peer Review

  • Online Retailer: Developing Classification, Regression, or Clustering Models

1 Labs

  • Course 4 Project

Auto Summary

Explore the essential concepts of machine learning with Coursera's "Train Machine Learning Models" course. Tailored for business professionals, this course delves into model hypothesis testing, training, tuning, and evaluation using algorithms for classification, regression, forecasting, and clustering. Ideal for those with a computing background, it spans 1740 minutes and offers a Starter subscription option. Enhance your data science skills and advance your AI proficiency with this professional-level course.

Stacey McBrine
Instructors

Stacey McBrine

Sarah Haq
Instructors

Sarah Haq