Build Decision Trees, SVMs, and Artificial Neural Networks

This course is part of CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

Instructor: Stacey McBrine

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4,758 already enrolled

5 modules

Gain insight into a topic and learn the fundamentals.

14 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

14 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Train and evaluate decision trees and random forests for regression and classification.
Train and evaluate support-vector machines (SVM) for regression and classification.
Train and evaluate multi-layer perceptron (ML) artificial neural networks (ANN) for regression and classification.
Train and evaluate convolutional neural networks (CNN) and recurrent neural networks (RNN) for computer vision and natural language processing tasks.

Skills you'll gain

Tools you'll learn

Classification Algorithms

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments¹

AI Graded see disclaimer

Taught in English

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Build your Machine Learning expertise

This course is part of the CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

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 5 modules in this course

There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more. Adding all of these algorithms to your skillset is crucial for selecting the best tool for the job.

This fourth and final course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate continues on from the previous course by introducing more, and in some cases, more advanced algorithms used in both machine learning and deep learning. As before, you'll build multiple models that can solve business problems, and you'll do so within a workflow. Ultimately, this course concludes the technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.

You've built machine learning models from fundamental linear regression and classification algorithms. These algorithms can get you pretty far in many scenarios, but they are not the only algorithms that can meet your needs. In this module, you'll build machine learning models from decision trees and random forests, two alternative approaches to solving regression and classification problems.

What's included

16 videos5 readings1 assignment1 discussion prompt2 ungraded labs

16 videos Total 64 minutes

Build Decision Trees, SVMs, and Artificial Neural Networks Course Introduction 3 minutes
CAIP Specialization Introduction 4 minutes
Build Decision Trees and Random Forests Module Introduction 1 minute
Decision Tree 3 minutes
Classification and Regression Tree (CART) 3 minutes
Gini Index Example 8 minutes
CART Hyperparameters 8 minutes
Pruning 4 minutes
C4.5 5 minutes
Bin Determination 3 minutes
One-Hot Encoding 3 minutes
Decision Trees Compared to Other Algorithms 2 minutes
Ensemble Learning 3 minutes
Random Forest 7 minutes
Random Forest Hyperparameters 3 minutes
Feature Selection Benefits 3 minutes

5 readings Total 20 minutes

Overview 2 minutes
Get help and meet other learners. Join your Community! 5 minutes
Decision Tree Algorithm Comparison 3 minutes
Guidelines for Building a Decision Tree Model 5 minutes
Guidelines for Building a Random Forest Model 5 minutes

1 assignment Total 30 minutes

Building Decision Trees and Random Forests 30 minutes

1 discussion prompt Total 5 minutes

Reflect on What You've Learned 5 minutes

2 ungraded labs Total 180 minutes

Building a Decision Tree Model 90 minutes
Building a Random Forest Model 90 minutes

Another alternative approach to regression and classification comes in the form of support-vector machines (SVMs). In this module, you'll build SVMs that can do a good job of handling outliers and tackling high-dimensional data in an efficient manner.

What's included

8 videos3 readings1 assignment1 discussion prompt2 ungraded labs

8 videos Total 35 minutes

Build Support-Vector Machines (SVM) Module Introduction 1 minute
Support-Vector Machines (SVMs) 2 minutes
SVMs for Linear Classification 3 minutes
Hard-Margin and Soft-Margin Classification 4 minutes
SVMs for Non-Linear Classification 1 minute
Kernel Trick 14 minutes
Kernel Methods 8 minutes
SVMs for Regression 2 minutes

3 readings Total 12 minutes

Overview 2 minutes
Guidelines for Building SVM Models for Classification 5 minutes
Guidelines for Building SVM Models for Regression 5 minutes

1 assignment Total 30 minutes

Building SVMs 30 minutes

1 discussion prompt Total 5 minutes

Reflect on What You've Learned 5 minutes

2 ungraded labs Total 105 minutes

Building an SVM Model for Classification 60 minutes
Building an SVM Model for Regression 45 minutes

All of the algorithms discussed thus far fall under the general umbrella of machine learning. While they are powerful and complex in their own right, the algorithms that make up the subdomain of deep learning—called artificial neural networks (ANNs)—are even more so. In this module, you'll build a fundamental version of an ANN called a multi-layer perceptron (MLP) that can tackle the same basic types of tasks (regression, classification, etc.), while being better suited to solving more complicated and data-rich problems.

What's included

8 videos2 readings1 assignment1 discussion prompt1 ungraded lab

8 videos Total 29 minutes

Build Multi-Layer Perceptrons (MLP) Module Introduction 1 minute
Artificial Neural Network (ANN) 2 minutes
Perceptron 6 minutes
Perceptron Training 7 minutes
Multi-Layer Perceptron (MLP) 4 minutes
ANN Layers 2 minutes
Backpropagation 3 minutes
Activation Functions 5 minutes

2 readings Total 7 minutes

Overview 2 minutes
Guidelines for Building MLPs 5 minutes

1 assignment Total 30 minutes

Building MLPs 30 minutes

1 discussion prompt Total 5 minutes

Reflect on What You've Learned 5 minutes

1 ungraded lab Total 90 minutes

Building an MLP 90 minutes

Now that you've built MLP neural networks, you can incorporate them into two wider architectures: convolutional neural networks (CNNs), which excel at solving computer vision problems; and recurrent neural networks (RNNs), which are most often used to process natural languages.

What's included

11 videos3 readings1 assignment1 discussion prompt2 ungraded labs

11 videos Total 66 minutes

Build Convolutional and Recurrent Neural Networks (CNN/RNN) Module Introduction 2 minutes
Convolutional Neural Network (CNN) 4 minutes
CNN Filters 7 minutes
Padding and Stride 3 minutes
CNN Architecture 10 minutes
Generative Adversarial Network (GAN) 6 minutes
Recurrent Neural Network (RNN) 6 minutes
Memory Cell 2 minutes
RNN Training 4 minutes
Long Short-Term Memory (LSTM) Cell 13 minutes
Embedding 8 minutes