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Build Decision Trees, SVMs, and Artificial Neural Networks

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Build Decision Trees, SVMs, and Artificial Neural Networks

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

Instructor: Stacey McBrine

4,836 already enrolled

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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.

Module details

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 videosTotal 64 minutes

Build Decision Trees, SVMs, and Artificial Neural Networks Course Introduction3 minutes
CAIP Specialization Introduction4 minutes
Build Decision Trees and Random Forests Module Introduction1 minute
Decision Tree3 minutes
Classification and Regression Tree (CART)3 minutes
Gini Index Example8 minutes
CART Hyperparameters8 minutes
Pruning4 minutes
C4.55 minutes
Bin Determination3 minutes
One-Hot Encoding3 minutes
Decision Trees Compared to Other Algorithms2 minutes
Ensemble Learning3 minutes
Random Forest7 minutes
Random Forest Hyperparameters3 minutes
Feature Selection Benefits3 minutes

5 readingsTotal 20 minutes

Overview2 minutes
Get help and meet other learners. Join your Community!5 minutes
Decision Tree Algorithm Comparison3 minutes
Guidelines for Building a Decision Tree Model5 minutes
Guidelines for Building a Random Forest Model5 minutes

1 assignmentTotal 30 minutes

Building Decision Trees and Random Forests30 minutes

1 discussion promptTotal 5 minutes

Reflect on What You've Learned5 minutes

2 ungraded labsTotal 180 minutes

Building a Decision Tree Model90 minutes
Building a Random Forest Model90 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 videosTotal 35 minutes

Build Support-Vector Machines (SVM) Module Introduction1 minute
Support-Vector Machines (SVMs)2 minutes
SVMs for Linear Classification3 minutes
Hard-Margin and Soft-Margin Classification4 minutes
SVMs for Non-Linear Classification1 minute
Kernel Trick14 minutes
Kernel Methods8 minutes
SVMs for Regression2 minutes

3 readingsTotal 12 minutes

Overview2 minutes
Guidelines for Building SVM Models for Classification5 minutes
Guidelines for Building SVM Models for Regression5 minutes

1 assignmentTotal 30 minutes

Building SVMs30 minutes

1 discussion promptTotal 5 minutes

Reflect on What You've Learned5 minutes

2 ungraded labsTotal 105 minutes

Building an SVM Model for Classification60 minutes
Building an SVM Model for Regression45 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 videosTotal 29 minutes

Build Multi-Layer Perceptrons (MLP) Module Introduction1 minute
Artificial Neural Network (ANN)2 minutes
Perceptron6 minutes
Perceptron Training7 minutes
Multi-Layer Perceptron (MLP)4 minutes
ANN Layers2 minutes
Backpropagation3 minutes
Activation Functions5 minutes

2 readingsTotal 7 minutes

Overview2 minutes
Guidelines for Building MLPs5 minutes

1 assignmentTotal 30 minutes

Building MLPs30 minutes

1 discussion promptTotal 5 minutes

Reflect on What You've Learned5 minutes

1 ungraded labTotal 90 minutes

Building an MLP90 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 videosTotal 66 minutes

Build Convolutional and Recurrent Neural Networks (CNN/RNN) Module Introduction2 minutes
Convolutional Neural Network (CNN)4 minutes
CNN Filters7 minutes
Padding and Stride3 minutes
CNN Architecture10 minutes
Generative Adversarial Network (GAN)6 minutes
Recurrent Neural Network (RNN)6 minutes
Memory Cell2 minutes
RNN Training4 minutes
Long Short-Term Memory (LSTM) Cell13 minutes
Embedding8 minutes