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Machine & Deep Learning Algorithms: Introduction

Machine & Deep Learning Algorithms: Introduction


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Examine the fundamentals of machine learning and how Pandas ML can be used to build ML models. The workings of Support Vector Machines to perform classification of data is also covered.



Expected Duration (hours)
0.8

Lesson Objectives

Machine & Deep Learning Algorithms: Introduction

  • recognize the different kinds of machine learning algorithms such as regression, classification, and clustering, as well as their specific applications
  • describe the process involved in learning a relationship between input and output during the training phase of machine learning
  • identify the benefits of combining Pandas, scikit-learn, and XGBoost into a single library to ease the task of building and evaluating ML models
  • describe what Support Vector Machines are and how they are used to find a hyperplane to divide data points into categories
  • recognize the problems associated with a model that is overfitted to training data and how to mitigate the issue
  • define what unsupervised learning is, list the features of SVMs, and describe the issues one may run into when using an overfitted model for predictions
  • Course Number:
    it_dsmdladj_01_enus

    Expertise Level
    Beginner