Course curriculum

    1. Lesson Introduction

    2. Learning Review

    3. Introduction to AI and Machine Learning

    4. AI Applications

    5. Moore's Law

    6. Discuss about Machine Learning

    7. Machine Learning Types

    8. Discuss about Linear Regression

    9. Graphing Math/Algebraic Functions

    10. Line of Best Fit

    11. Line of Best Fit continued

    12. Prediction with the Model

    13. Model Fits

    14. Neural Networks Explanation

    1. Setup Kaggle

    2. Previous Chapter Review

    3. Different Models

    4. Selecting a Model for Our Task

    5. Setting Notebook Appearance

    6. Problem Explanation

    7. Problem Explanation continued

    8. Notebook Setup

    9. Data Inspection

    10. Applying Data to Model

    11. Converting Categorical Data to Calculable Data

    12. Converting Categorical Data to Calculable Data continued

    13. Trimming and Renaming DataFrame Columns

    14. Splitting Data to Train/Test Sets

    15. Chapter Review

    1. Chapter Introduction

    2. Chapter Introduction continued

    3. Chapter Introduction continued

    4. Explaining Training/Test Data

    5. Cast Pandas Data to Numpy Data

    6. Explain Numpy Data Structure

    7. Explain Numpy Data Structure continued

    8. Selecting And Visualizing Data for Model

    9. Visualizing Data for Model continued

    10. Choosing a Model

    11. Transforming Data for Model

    12. Fitting the Model and Getting Output

    13. Plotting and Scoring the Model

About this course

  • $295.00
  • 42 lessons
  • 3.5 hours of video content