Build Decision Trees, SVMs, and Artificial Neural Networks

Instructor: Stacey McBrine

Intermediate Level • 21 hours to complete 3 weeks at 7 hours a week • Flexible Schedule

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

Supervised Learning
Statistical Machine Learning
Applied Machine Learning
Random Forest Algorithm
Artificial Intelligence and Machine Learning (AI/ML)
Regression Analysis
Natural language processing
Decision Tree Learning
Deep Learning
Artificial Neural Networks
Machine Learning Algorithms
Computer Vision

Shareable Certificate

Earn a shareable certificate to add to your LinkedIn profile

Outcomes

  • 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

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.

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.

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.

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.

You'll work on a project in which you'll apply your knowledge of the material in this course to a practical scenario.