Course curriculum

    1. Defining Machine Learning

    2. The Different Applications of Machine Learning

    3. The Role of a Learning Agent

    4. Deep Learning

    5. The purpose of a Neural Network

    6. Example of a Neural Network with Voice Recognition

    7. How Machine Learning Compliments Knowledge-Based Systems

    8. The Machine Learning Process of Working with Data

    9. Data Preprocessing

    10. Selecting a Machine Learning Model

    11. Reviewing the Algorithm and Machine Learning Model

    12. Three Steps to Test Data in the Machine Learning Process

    1. Object-Oriented Programming Languages Used in Machine Learning

    2. Coding for Machine Learning

    1. Bayes Theorem

    2. Linear Algebra

    3. Statistics

    4. Regression Analysis 1/2

    5. What is a Statistical Difference?

    6. Regression Analysis 2/2

    7. Common Algorithms Used in Supervised and Unsupervised Machine Learning

    8. Boosting, Decision Forests, and Ensembles

    9. Supervised, Unsupervised and Semi-Supervised Learning

    1. Detection of Credit Card Fraud in Machine Learning

    2. An Action List for Preparing Data Using Machine Learning

    3. The Process of Training a Machine Learning Model

    4. The process of testing a Machine Learning model

    5. A Checklist to Evaluate the Results of Testing

About this course

  • £375.00
  • 28 lessons
  • 0 hours of video content

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