Build Regression, Classification, and Clustering Models
- Offered byCoursera
- Public/Government Institute
Build Regression, Classification, and Clustering Models at Coursera Overview
Duration | 20 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Build Regression, Classification, and Clustering Models at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 5 in the CertNexus Certified Artificial Intelligence Practitioner
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level ML workflow knowledge is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.
- Approx. 20 hours to complete
- English Subtitles: English
Build Regression, Classification, and Clustering Models at Coursera Course details
- 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.
Build Regression, Classification, and Clustering Models at Coursera Curriculum
Build Linear Regression Models Using Linear Algebra
Course Intro: Build Regression, Classification, and Clustering Models
Build Linear Regression Models Using Linear Algebra Module Introduction
Linear Regression
Linear Equation
Straight Line Fit to Data Example
Linear Regression in Machine Learning
Matrices in Linear Regression
Normal Equation
Advanced Linear Models
Cost Function
MSE and MAE
Coefficient of Determination
Normal Equation Shortcomings
Overview
Guidelines for Building a Regression Model Using Linear Algebra
Building Linear Regression Models Using Linear Algebra
Build Regularized and Iterative Linear Regression Models
Build Regularized and Iterative Linear Regression Models Module Introduction
Regularization Techniques
Ridge Regression
Lasso Regression
Elastic Net Regression
Iterative Models
Gradient Descent
Gradient Descent Techniques
Overview
Guidelines for Building a Regularized Linear Regression Model
Guidelines for Building an Iterative Linear Regression Model
Building Regularized and Iterative Linear Regression Models
Train Classification Models
Train Classification Models Module Introduction
Linear Regression Shortcomings
Logistic Regression
Decision Boundary
Cost Function for Logistic Regression
k-Nearest Neighbor (k-NN)
Logistic Regression vs. k-NN
Multi-Label and Multi-Class Classification
Multinomial Logistic Regression
Overview
Guidelines for Training Binary Classification Models
Guidelines for Training Multi-Class Classification Models
Training Classification Models
Evaluate and Tune Classification Models
Evaluate and Tune Classification Models Module Introduction
Model Performance
Confusion Matrix
Classifier Performance Measurement
Accuracy
Precision
Recall
F? Score
Receiver Operating Characteristic (ROC) Curve
Thresholds and AUC
Precision?Recall Curve (PRC)
Hyperparameter Optimization
Grid Search
Randomized Search
Bayesian Optimization
Genetic Algorithms
Overview
Guidelines for Evaluating Classification Models
Guidelines for Tuning Classification Models
Evaluating and Tuning Classification Models
Build Clustering Models
Build Clustering Models Module Introduction
k-Means Clustering
Global vs. Local Optimization
Elbow Point
Cluster Sum of Squares
Silhouette Analysis
k-Means Clustering Shortcomings
Hierarchical Clustering
Dendrogram
Overview
Additional Cluster Analysis Methods
Guidelines for Building a k-Means Clustering Model
Guidelines for Building a Hierarchical Clustering Model
Building Clustering Models
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