Table of Contents
Preface
Part 1: The Data Science Landscape – Open Source to the Rescue
Chapter 1: Understanding the AI/ML landscape
Introducing Artificial Intelligence (AI)
Defining AI
Defining a data scientist
Understanding the current state of AI and ML
Knowing the difference between AI and ML
Understanding the massive generation of new data
Evaluating how AI delivers business value
Understanding the main types of ML models
Supervised learning
Unsupervised learning
Reinforcement learning
Evaluating the problem type
Dealing with out-of-date models
Difference between online and batch learning
How models become stale: model drift
Installing packages with Anaconda
How to use Anaconda Individual Edition to download packages
How to handle dependencies ...
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