Build Regression, Classification, and Clustering Models
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Build Regression, Classification, and Clustering Models
This course is part of CertNexus Certified Artificial Intelligence Practitioner Professional Certificate
Instructor: Anastas Stoyanovsky
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What you'll learn
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.
Skills you'll gain
Tools you'll learn
Details to know
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Build your Machine Learning expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from CertNexus
There are 6 modules in this course
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.
In the preceding course, you went through the overall machine learning workflow from start to finish. Now it's time to start digging into the algorithms that make up machine learning. This will help you select the most appropriate algorithm(s) for your own purposes, as well as how best to apply them to solve a problem. A good place to start is with simple linear regression.
What's included
13 videos3 readings1 assignment1 discussion prompt1 ungraded lab
13 videos•Total 32 minutes
- Course Intro: Build Regression, Classification, and Clustering Models•3 minutes
- Build Linear Regression Models Using Linear Algebra Module Introduction•1 minute
- Linear Regression•1 minute
- Linear Equation•3 minutes
- Straight Line Fit to Data Example•2 minutes
- Linear Regression in Machine Learning•3 minutes
- Matrices in Linear Regression•4 minutes
- Normal Equation•5 minutes
- Advanced Linear Models•2 minutes
- Cost Function•1 minute
- MSE and MAE•3 minutes
- Coefficient of Determination•1 minute
- Normal Equation Shortcomings•1 minute
3 readings•Total 17 minutes
- Overview•2 minutes
- Get help and meet other learners. Join your Community!•5 minutes
- Guidelines for Building a Regression Model Using Linear Algebra•10 minutes
1 assignment•Total 30 minutes
- Building Linear Regression Models Using Linear Algebra•30 minutes
1 discussion prompt•Total 10 minutes
- Reflect on What You've Learned•10 minutes
1 ungraded lab•Total 60 minutes
- Building a Regression Model Using Linear Algebra•60 minutes
The simple model you created earlier works well in many cases, but that doesn't mean it's the optimal approach. Linear regression can be enhanced by the process of regularization, which will often improve the skill of your machine learning model. In addition, an iterative approach to regression can take over where the closed-form solution falls short. In this module, you'll apply both techniques.
What's included
8 videos3 readings1 assignment1 discussion prompt2 ungraded labs
8 videos•Total 14 minutes
- Build Regularized and Iterative Linear Regression Models Module Introduction•1 minute
- Regularization Techniques•1 minute
- Ridge Regression•3 minutes
- Lasso Regression•1 minute
- Elastic Net Regression•2 minutes
- Iterative Models•1 minute
- Gradient Descent•3 minutes
- Gradient Descent Techniques•1 minute
3 readings•Total 12 minutes
- Overview•2 minutes
- Guidelines for Building a Regularized Linear Regression Model•5 minutes
- Guidelines for Building an Iterative Linear Regression Model•5 minutes
1 assignment•Total 30 minutes
- Building Regularized and Iterative Linear Regression Models•30 minutes
1 discussion prompt•Total 5 minutes
- Reflect on What You've Learned•5 minutes
2 ungraded labs•Total 120 minutes
- Building a Regularized Linear Regression Model•75 minutes
- Building an Iterative Linear Regression Model•45 minutes
Besides linear regression, the other major type of supervised machine learning outcome is classification. To begin with, you'll train some binary classification models using a few different algorithms. Then, you'll train a model to handle cases in which there are multiple ways to classify a data example. Each algorithm may be ideal for solving a certain type of classification problem, so you need to be aware of how they differ.
What's included
9 videos3 readings1 assignment1 discussion prompt2 ungraded labs
9 videos•Total 13 minutes
- Train Classification Models Module Introduction•1 minute
- Linear Regression Shortcomings•1 minute
- Logistic Regression•1 minute
- Decision Boundary•1 minute
- Cost Function for Logistic Regression•1 minute
- k-Nearest Neighbor (k-NN)•3 minutes
- Logistic Regression vs. k-NN•1 minute
- Multi-Label and Multi-Class Classification•1 minute
- Multinomial Logistic Regression•2 minutes
3 readings•Total 15 minutes
- Overview•2 minutes
- Guidelines for Training Binary Classification Models•10 minutes
- Guidelines for Training Multi-Class Classification Models•3 minutes
1 assignment•Total 30 minutes
- Training Classification Models•30 minutes
1 discussion prompt•Total 5 minutes
- Reflect on What You've Learned•5 minutes
2 ungraded labs•Total 135 minutes
- Training Binary Classification Models•90 minutes
- Training a Multi-Class Classification Model•45 minutes
It's not enough to just train a model you think is best, and then call it a day. Unless you're using a very simple dataset or you get lucky, the default parameters aren't going to give you the best possible model for solving the problem. So, in this module, you'll evaluate your classification models to see how they're performing, then you'll attempt to improve their skill.
What's included
16 videos3 readings1 assignment1 discussion prompt2 ungraded labs
16 videos•Total 27 minutes
- Evaluate and Tune Classification Models Module Introduction•1 minute
- Model Performance•1 minute
- Confusion Matrix•1 minute
- Classifier Performance Measurement•1 minute
- Accuracy•1 minute
- Precision•1 minute
- Recall•2 minutes
- F₁ Score•2 minutes
- Receiver Operating Characteristic (ROC) Curve•2 minutes
- Thresholds and AUC•3 minutes
- Precision–Recall Curve (PRC)•2 minutes
- Hyperparameter Optimization•1 minute
- Grid Search•2 minutes
- Randomized Search•2 minutes
- Bayesian Optimization•2 minutes
- Genetic Algorithms•3 minutes
3 readings•Total 22 minutes
- Overview•2 minutes
- Guidelines for Evaluating Classification Models•10 minutes
- Guidelines for Tuning Classification Models•10 minutes
1 assignment•Total 30 minutes
- Evaluating and Tuning Classification Models•30 minutes
1 discussion prompt•Total 5 minutes
- Reflect on What You've Learned•5 minutes
2 ungraded labs•Total 90 minutes
- Evaluating a Classification Model•45 minutes
- Tuning a Classification Model•45 minutes
You've built models to tackle linear regression problems and classification problems. One of the other major machine learning tasks that you might want to engage in is clustering, a form of unsupervised learning. In this module, you'll see how a machine learning model can help you identify useful patterns even when the data you have to work with isn't labeled.
What's included
9 videos4 readings1 assignment1 discussion prompt2 ungraded labs
9 videos•Total 18 minutes
- Build Clustering Models Module Introduction•1 minute
- k-Means Clustering•2 minutes
- Global vs. Local Optimization•2 minutes
- Elbow Point•1 minute
- Cluster Sum of Squares•2 minutes
- Silhouette Analysis•3 minutes
- k-Means Clustering Shortcomings•1 minute
- Hierarchical Clustering•3 minutes
- Dendrogram•2 minutes
4 readings•Total 20 minutes
- Overview•2 minutes
- Additional Cluster Analysis Methods•3 minutes
- Guidelines for Building a k-Means Clustering Model•10 minutes
- Guidelines for Building a Hierarchical Clustering Model•5 minutes
1 assignment•Total 30 minutes
- Building Clustering Models•30 minutes
1 discussion prompt•Total 5 minutes
- Reflect on What You've Learned•5 minutes
2 ungraded labs•Total 135 minutes
- Building a k-Means Clustering Model•90 minutes
- Building a Hierarchical Clustering Model•45 minutes
You'll work on a project in which you'll apply your knowledge of the material in this course to practical scenarios.
What's included
1 peer review1 ungraded lab
1 peer review•Total 300 minutes
- Building a Regression, Classification, or Clustering Model•300 minutes
1 ungraded lab•Total 10 minutes
- Course 3 Project•10 minutes
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