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Build Regression, Classification, and Clustering Models
  • Level Professional
  • Duration 20 hours
  • Course by CertNexus
  • Offered by

About

In most cases, the ultimate goal of a machine learning project is to produce a model. Models make decisions, predictions—anything that can help the business understand itself, its customers, and its environment better than a human could. Models are constructed using algorithms, and in the world of machine learning, there are many different algorithms to choose from. You need to know how to select the best algorithm for a given job, and how to use that algorithm to produce a working model that provides value to the business. This third course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate introduces you to some of the major machine learning algorithms that are used to solve the two most common supervised problems: regression and classification, and one of the most common unsupervised problems: clustering. You'll build multiple models to address each of these problems using the machine learning workflow you learned about in the previous course. Ultimately, this course begins a technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.

Modules

Overview

    • 2 Videos
    • 2 Readings
Show info about module content

2 Videos

  • Course Intro: Build Regression, Classification, and Clustering Models
  • Build Linear Regression Models Using Linear Algebra Module Introduction

2 Readings

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

Linear Algebra and Linear Regression

    • 7 Videos
Show info about module content

7 Videos

  • Linear Regression
  • Linear Equation
  • Straight Line Fit to Data Example
  • Linear Regression in Machine Learning
  • Matrices in Linear Regression
  • Normal Equation
  • Advanced Linear Models

Evaluate Linear Regression Models

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

1 Labs

  • Building a Regression Model Using Linear Algebra

4 Videos

  • Cost Function
  • MSE and MAE
  • Coefficient of Determination
  • Normal Equation Shortcomings

1 Readings

  • Guidelines for Building a Regression Model Using Linear Algebra

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Building Linear Regression Models Using Linear Algebra

1 Discussions

  • Reflect on What You've Learned

Overview

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Build Regularized and Iterative Linear Regression Models Module Introduction

1 Readings

  • Overview

Build Regularized Regression Models

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

1 Labs

  • Building a Regularized Linear Regression Model

4 Videos

  • Regularization Techniques
  • Ridge Regression
  • Lasso Regression
  • Elastic Net Regression

1 Readings

  • Guidelines for Building a Regularized Linear Regression Model

Build Iterative Regression Models

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

1 Labs

  • Building an Iterative Linear Regression Model

3 Videos

  • Iterative Models
  • Gradient Descent
  • Gradient Descent Techniques

1 Readings

  • Guidelines for Building an Iterative Linear Regression Model

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Building Regularized and Iterative Linear Regression Models

1 Discussions

  • Reflect on What You've Learned

Overview

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Train Classification Models Module Introduction

1 Readings

  • Overview

Train Binary Classification Models

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

1 Labs

  • Training Binary Classification Models

6 Videos

  • Linear Regression Shortcomings
  • Logistic Regression
  • Decision Boundary
  • Cost Function for Logistic Regression
  • k-Nearest Neighbor (k-NN)
  • Logistic Regression vs. k-NN

1 Readings

  • Guidelines for Training Binary Classification Models

Train Multi-Class Classification Models

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

1 Labs

  • Training a Multi-Class Classification Model

2 Videos

  • Multi-Label and Multi-Class Classification
  • Multinomial Logistic Regression

1 Readings

  • Guidelines for Training Multi-Class Classification Models

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Training Classification Models

1 Discussions

  • Reflect on What You've Learned

Overview

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Evaluate and Tune Classification Models Module Introduction

1 Readings

  • Overview

Evaluate Classification Models

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

1 Labs

  • Evaluating a Classification Model

10 Videos

  • Model Performance
  • Confusion Matrix
  • Classifier Performance Measurement
  • Accuracy
  • Precision
  • Recall
  • F₁ Score
  • Receiver Operating Characteristic (ROC) Curve
  • Thresholds and AUC
  • Precision–Recall Curve (PRC)

1 Readings

  • Guidelines for Evaluating Classification Models

Tune Classification Models

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

1 Labs

  • Tuning a Classification Model

5 Videos

  • Hyperparameter Optimization
  • Grid Search
  • Randomized Search
  • Bayesian Optimization
  • Genetic Algorithms

1 Readings

  • Guidelines for Tuning Classification Models

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Evaluating and Tuning Classification Models

1 Discussions

  • Reflect on What You've Learned

Overview

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Build Clustering Models Module Introduction

1 Readings

  • Overview

Build k-Means Clustering Models

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

1 Labs

  • Building a k-Means Clustering Model

5 Videos

  • k-Means Clustering
  • Global vs. Local Optimization
  • Elbow Point
  • Cluster Sum of Squares
  • Silhouette Analysis

2 Readings

  • Additional Cluster Analysis Methods
  • Guidelines for Building a k-Means Clustering Model

Build Hierarchical Clustering Models

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

1 Labs

  • Building a Hierarchical Clustering Model

3 Videos

  • k-Means Clustering Shortcomings
  • Hierarchical Clustering
  • Dendrogram

1 Readings

  • Guidelines for Building a Hierarchical Clustering Model

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Building Clustering Models

1 Discussions

  • Reflect on What You've Learned

Project

    • 1 PeerReview
    • 1 Lab
Show info about module content

1 Peer Review

  • Building a Regression, Classification, or Clustering Model

1 Labs

  • Course 3 Project

Auto Summary

Dive into the world of machine learning with "Build Regression, Classification, and Clustering Models," a professional course in Data Science & AI led by Coursera. Over 1200 minutes, master key algorithms for regression, classification, and clustering to create impactful models. Ideal for professionals, this course is part of the Certified Artificial Intelligence Practitioner certificate and offers flexible subscription options.

Anastas Stoyanovsky
Instructor

Anastas Stoyanovsky