Python Programming and Data Science Foundations for AI

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Overview

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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course provides a comprehensive foundation in Python programming and data science, essential for building AI applications. You will gain hands-on experience in Python fundamentals, explore essential data science tools like NumPy and Pandas, and develop an understanding of core machine learning concepts. Throughout the course, you’ll progress step by step, starting with Python basics such as control flow, functions, and data structures, then moving on to more advanced topics like object-oriented programming (OOP), data science libraries, and visualization tools. The course integrates interactive exercises to deepen your understanding, with real-world projects to apply what you've learned. The course is designed to be approachable for beginners, with no prior experience required. As you advance, you’ll build practical skills and a portfolio of projects, including Python applications, web apps, data analysis, and more. This hands-on approach ensures that you’ll not only learn but also apply the concepts to real-world AI challenges. By the end of the course, you will be able to write Python programs, manipulate data with libraries like Pandas, use statistical and machine learning techniques, and build data-driven applications to solve real-world problems.

Syllabus

Python Programming Basics for Artificial Intelligence

In this module, we will dive into Python programming basics, focusing on the foundational knowledge needed for AI development. You will learn essential Python syntax, control flow, functions, and data structures. The module also covers file handling and Pythonic coding practices, equipping you with practical skills for building AI applications.

Data Science Essentials for Artificial Intelligence

In this module, we will introduce the core concepts of data science, essential for artificial intelligence. You will explore libraries like NumPy and Pandas for data manipulation and analysis, learn how to clean and prepare data, and visualize data using powerful tools like Matplotlib and Seaborn. By the end of the module, you’ll be ready to perform exploratory data analysis on real datasets.

Mathematics for Machine Learning and Artificial Intelligence

In this module, we will cover the mathematical foundations essential for machine learning and AI. You will explore linear algebra, calculus, probability theory, and statistics, all of which are vital for understanding and optimizing machine learning algorithms. The module will also include a hands-on project to apply these concepts in building a linear regression model from scratch.

Probability and Statistics for Machine Learning and AI

In this module, we will dive into the probability and statistics methods used in machine learning to make predictions and draw conclusions. You will learn how to apply probability distributions, perform hypothesis testing, and analyze data relationships. The module also includes a project where you’ll analyze real-world data using statistical methods.

Python Basics

In this module, we will introduce the basics of Python programming, starting with simple syntax and building up to creating interactive programs. You will learn about variables, data types, loops, and functions, all while working on hands-on projects like a calculator, number comparison tool, and more.

Intermediate Python

In this module, we will delve into intermediate Python concepts, enhancing your skills in application development. You’ll work with advanced data structures, error handling, file manipulation, and external libraries, applying your knowledge to projects like a student grade manager and contact book app.

Working with Data

In this module, we will explore how to work with data using Python’s file handling capabilities and web scraping techniques. You’ll develop projects like a weather app, event countdown timer, and stock price tracker, gaining hands-on experience with reading, writing, and processing real-world data.

Object-Oriented Programming

In this module, we will focus on object-oriented programming (OOP) principles, including classes, inheritance, polymorphism, and encapsulation. You’ll build real-world applications like a bank account simulator, library management system, and mini ATM machine, all while mastering OOP concepts.

GUI Programming

In this module, we will introduce graphical user interface (GUI) programming using Python’s Tkinter library. You will learn how to build interactive applications, including a click counter, BMI calculator, and to-do list app, and finish the module with a complete expense tracker project.

Web Development with Python

In this module, we will dive into web development with Python using Flask. You’ll learn how to build web applications from scratch, handle user input with forms, and integrate databases. By the end of the module, you’ll deploy your projects to the web, including a personal portfolio website.

Data Science Basics

In this module, we will focus on the basics of data science, teaching you how to work with libraries like NumPy, Pandas, and Matplotlib. You’ll build projects such as a stock price tracker and COVID-19 dashboard, using data analysis techniques to track and visualize trends.