Build Regression, Classification, and Clustering Models
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Approx. 20 hours to complete
Course Summary
Learn to build regression, classification, and clustering models in this comprehensive course. Gain practical experience with real-world datasets and apply your knowledge to solve business problems.Key Learning Points
- Learn to build machine learning models from scratch
- Gain experience with real-world datasets
- Apply machine learning to solve real business problems
Job Positions & Salaries of people who have taken this course might have
- USA: $113,000
- India: ₹1,000,000
- Spain: €45,000
- USA: $113,000
- India: ₹1,000,000
- Spain: €45,000
- USA: $120,000
- India: ₹1,200,000
- Spain: €50,000
- USA: $113,000
- India: ₹1,000,000
- Spain: €45,000
- USA: $120,000
- India: ₹1,200,000
- Spain: €50,000
- USA: $75,000
- India: ₹700,000
- Spain: €30,000
Related Topics for further study
Learning Outcomes
- Build regression, classification, and clustering models from scratch
- Apply machine learning to solve business problems
- Gain practical experience with real-world datasets
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming
- Familiarity with statistics and linear algebra
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Machine Learning
- Data Science Methodology
Related Education Paths
Notable People in This Field
- Co-founder, Coursera
- Co-founder, Coursera
Related Books
Description
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.
Knowledge
- Train and evaluate linear regression models.
- Train binary and multi-class classification models.
- Evaluate and tune classification models to improve their performance.
- Train and evaluate clustering models to find useful patterns in unsupervised data.
Outline
- Build Linear Regression Models Using Linear Algebra
- Build Regression, Classification, and Clustering Models Course Introduction
- CAIP Specialization Introduction
- 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
- F₁ 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
- Apply What You've Learned
Summary of User Reviews
Learn how to build regression, classification, and clustering models in this Coursera course. Users have praised the course for its practical approach and real-world examples.Key Aspect Users Liked About This Course
practical approach and real-world examplesPros from User Reviews
- Instructors provide clear explanations and make complex topics easy to understand
- Hands-on exercises give learners the opportunity to apply what they've learned
- Course content is relevant and up-to-date with industry standards
- Flexible schedule allows learners to study at their own pace
- Course is suitable for beginners with no prior experience in data science
Cons from User Reviews
- Some users found the course too basic and not challenging enough
- Course materials can be disorganized and hard to navigate
- Limited interaction with instructors and other learners
- Lack of practical projects or real-world examples beyond the exercises
- Course may not cover more advanced topics in depth
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