Course curriculum

    1. Course Introduction

    2. Download PDF of Book Here

    3. The AI Hierarchy

    4. Two Types of Interviews

    5. The Three Core Careers

    6. Interview Questions

    1. Section Overview

    2. Five Common Job Themes

    3. Why Python?

    4. Machine Learning Nomenclature

    5. Types of Machine Learning

    6. The Machine Learning Process

    7. Interview Questions (Core Vernacular and the Machine Learning Process)

    8. Data Wrangling Process

    9. The Array

    10. Interview Questions (Imputation and Arrays)

    11. NumPy Crash Course

    1. Section Overview

    2. Interview Questions (Python)

    3. Interview Questions (More Python Questions)

    4. No Deep Learning Frameworks or Libraries

    5. Interview Questions (Pandas)

    6. Interview Questions (SciKit-Learn)

    7. Interview Questions (NumPy)

    1. Section Introduction

    2. Two Types of Data

    3. Databases

    4. Table Relationships

    5. Manipulating Data

    6. Table Joins

    1. Section Introduction

    2. Statistics and Machine Learning

    3. Interview Questions (Basic Statistics)

    4. Measures of Central Tendency

    5. Law of Large Numbers

    6. Interview Questions (MOCT,MOV)

    7. Measures of Variability

    8. Rescaling

    9. Interview Questions (Rescaling)

    10. Outliers and Imputation

    11. One-Hot Encoding

    12. Bias-Variance Tradeoff

    1. Section Introduction

    2. Machine Learning Models

    3. Common Modeling Problems

    4. Classification Metrics

    5. Interview Questions (Classification Metrics)

    6. Interview Questions (Regression Metrics)

    7. Bagging and Boosting

    8. XGBoost

    9. Interview Questions (Bagging, Boosting and XGBoost)

    10. Artificial Neural Networks

    11. Interview Questions (ANNs and Deep Learning)

About this course

  • Free
  • 53 lessons
  • 2.5 hours of video content