Foundations of AI Engineering

Übersicht

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In this course, you will gain a comprehensive foundation in AI engineering, starting with the fundamentals of Python programming and advancing through key data science and machine learning concepts. The course emphasizes hands-on projects that will solidify your understanding of these essential skills, providing a deep dive into Python, data science tools, and mathematics necessary for machine learning.

By mastering these core concepts, you'll be equipped to approach AI engineering challenges confidently. The course is structured to guide you through each key area, beginning with Python programming basics.

You will learn how to work with Python syntax, data structures, functions, and file handling, all necessary for real-world applications. As you progress, you'll explore data science essentials using NumPy and Pandas, working on projects that teach you data manipulation, visualization, and analysis.

The course culminates with a deeper dive into the mathematics required for machine learning, including linear algebra, calculus, and probability. This course is perfect for aspiring AI engineers, data scientists, and those interested in pursuing machine learning.

No prior experience is required, though a basic understanding of programming and mathematics will be helpful. The course is designed for beginners but includes complex mathematical concepts for those ready to delve deeper.

By the end of the course, you will be able to write Python code for AI-related applications, clean and manipulate data using Pandas, visualize data with Matplotlib, apply machine learning math concepts, and execute probability and statistics techniques in data analysis and model-building projects.

Lehrplan

• Week 1: Python Programming Basics
In this module, we will introduce you to the fundamental concepts of Python programming, including development setup and basic syntax. You will explore control flow, functions, and data structures while applying your knowledge in hands-on projects. By the end, you'll be ready to write efficient, Pythonic code.
• Week 2: Data Science Essentials
In this module, we will cover the essential tools for data science, from NumPy for numerical operations to Pandas for data manipulation. You'll also gain skills in data visualization and work on an EDA project, applying your knowledge to extract insights from real-world datasets.
• Week 3: Mathematics for Machine Learning
In this module, we will dive into the mathematics behind machine learning, starting with linear algebra and advancing to calculus concepts. You’ll understand the mathematical foundation needed for building and optimizing machine learning models, while applying this knowledge to create your own linear regression model.
• Week 4: Probability and Statistics for Machine Learning
In this module, we will explore the critical concepts of probability and statistics used in machine learning. From probability theory to hypothesis testing, you will gain the tools needed to analyze and interpret data. The module also includes a hands-on project to apply these concepts to real-world data.

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Unterrichtet von

Packt - Course Instructors

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Fachgebiete

Programming