Python Essentials for AI: A Practical Guide

Unlock the Power of AI with Python: Master the Language at the Heart of Machine Learning

Venelin Valkov

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Girl learning the basics of Python

Welcome to the Python Essentials for AI Engineers, a journey designed to equip you with the Python skills crucial for thriving in the world of artificial intelligence. In this tutorial, you’ll gain hands-on experience with real-world exercises, emphasizing the practical application of Python in AI. Get ready to transform theoretical knowledge into actionable insights, preparing you to meet the demands of AI with the skill and creativity of an expert.

Mastering the essentials of Python is crucial for creating real-world AI and Machine Learning (ML) projects due to Python’s widespread adoption and its rich ecosystem of libraries and tools specifically designed for these fields. Python’s simplicity and readability make it accessible for newcomers, while its powerful features like data handling, numerical computation, and machine learning libraries enable rapid development and testing of complex models.

Data Structures

In the field of Artificial Intelligence (AI), managing and processing data effectively is vital. Python, with its robust data structures, is an excellent tool for AI Engineers. Let’s dive into how Python’s data structures can be utilized in practical AI scenarios, illustrated through relatable examples.

Lists for Feature Storage

Lists in Python are crucial for dynamically storing and manipulating features in data preprocessing, a key step in building AI models.

# Storing features of a machine learning model
model_features = ['age', 'income', 'education_level']

# Adding a new feature
model_features.append('marital_status')
model_features
['age', 'income', 'education_level', 'marital_status']

Dictionaries for Mapping Data

Dictionaries are crucial for creating and modifying mappings, such as linking features to their importance scores in models, aiding in feature selection and model interpretation.

# Mapping features to their importance scores
feature_importance = {"age": 0.75, "income": 0.85, "education_level": 0.65}

# Accessing…

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