Course curriculum

  • 1

    Introduction

    • Course Introduction

    • Download PDF of Book Here

    • The AI Hierarchy

    • Two Types of Interviews

    • The Three Core Careers

    • Interview Questions

  • 2

    Machine Learning Concepts

    • Section Overview

    • Five Common Job Themes

    • Why Python?

    • Machine Learning Nomenclature

    • Types of Machine Learning

    • The Machine Learning Process

    • Interview Questions (Core Vernacular and the Machine Learning Process)

    • Data Wrangling Process

    • The Array

    • Interview Questions (Imputation and Arrays)

  • 3

    Python and the Core Machine Learning Libraries

    • Section Overview

    • Interview Questions (Python)

    • Interview Questions (More Python Questions)

    • No Deep Learning Frameworks or Libraries

    • Interview Questions (Pandas)

    • Interview Questions (SciKit-Learn)

    • Interview Questions (NumPy)

  • 4

    Working with Data

    • Section Introduction

    • Two Types of Data

    • Databases

    • Table Relationships

    • Manipulating Data

    • Table Joins

  • 5

    Statistics in Machine Learning

    • Section Introduction

    • Statistics and Machine Learning

    • Interview Questions (Basic Statistics)

    • Measures of Central Tendency

    • Law of Large Numbers

    • Interview Questions (MOCT,MOV)

    • Measures of Variability

    • Rescaling

    • Interview Questions (Rescaling)

    • Outliers and Imputation

    • One-Hot Encoding

    • Bias-Variance Tradeoff

  • 6

    Modeling

    • Section Introduction

    • Machine Learning Models

    • Common Modeling Problems

    • Classification Metrics

    • Interview Questions (Classification Metrics)

    • Interview Questions (Regression Metrics)

    • Bagging and Boosting

    • XGBoost

    • Interview Questions (Bagging, Boosting and XGBoost)

    • Artificial Neural Networks

    • Interview Questions (ANNs and Deep Learning)