Python with AI Course Syllabus

The following is a suggested syllabus for a Python with AI training program. The actual syllabus should be confirmed with HSK SoftAcademy before you publish it as a promise to students.

Module 1: Python Programming Fundamentals

Learn the basic concepts needed to write Python programs.

Topics may include:

1. Introduction to Python and its applications.
2. Installing Python and setting up a development environment.
3. Writing and running your first Python program.
4. Variables and data types.
5. Strings, numbers, and Boolean values.
6. Arithmetic, comparison, and logical operators.
7. User input and program output.
8. Conditional statements.
9. For and while loops.
10. Functions and parameters.
11. Lists, tuples, sets, and dictionaries.
12. Basic file handling.
13. Introduction to error handling.

Practice projects:

1. Simple calculator.
2. Student marks calculator.
3. Number guessing game.
4. Basic expense tracker.

These exercises help learners build confidence with Python before moving to data processing and AI.

Module 2: Object-Oriented Programming and Code Organization

Object-oriented programming helps organize code into classes and objects. It is useful when building larger applications.

Topics may include:

1. Classes and objects.
2. Constructors and methods.
3. Attributes and instance variables.
4. Encapsulation basics.
5. Inheritance and polymorphism.
6. Modules and packages.
7. Code reuse.
8. Error handling and debugging.
9. Writing readable and maintainable code.

Practice project: Create a simple student or employee record application using classes.

Module 3: Data Handling with Python

Data is an important part of many AI and machine learning applications. Learners need to understand how information is stored, cleaned, and prepared.

Topics may include:

1. Introduction to structured data.
2. Reading and writing CSV files.
3. Working with tables and records.
4. Introduction to NumPy.
5. Introduction to Pandas.
6. DataFrames and basic data operations.
7. Handling missing or incorrect values.
8. Filtering and sorting data.
9. Basic summaries and statistics.
10. Introduction to charts and visualizations.

Practice projects:

1. Analyze a sample sales dataset.
2. Create a report from student marks.
3. Summarize monthly expenses.
4. Visualize simple data using charts.

These activities introduce learners to the data preparation tasks often needed before applying machine learning.

Module 4: Introduction to Artificial Intelligence

Understand what AI is and how it differs from traditional programming.

Topics may include:

1. Meaning of artificial intelligence.
2. Common uses of AI.
3. Traditional programming versus AI-based systems.
4. Introduction to machine learning.
5. Introduction to deep learning.
6. Introduction to generative AI.
7. AI models and training data.
8. Basic AI terminology.
9. AI strengths and limitations.
10. Responsible and safe use of AI tools.

Learners should understand that AI systems can make mistakes. Their outputs need to be reviewed, especially when accuracy matters.

Module 5: Machine Learning Fundamentals

Machine learning is a branch of AI in which systems learn patterns from data to make predictions or decisions.

A beginner-level module may include:

1. Introduction to machine learning.
2. Supervised and unsupervised learning.
3. Features and target values.
4. Training and testing data.
5. Introduction to classification.
6. Introduction to regression.
7. Model training and prediction.
8. Basic model evaluation.
9. Understanding overfitting at an introductory level.
10. Introduction to scikit-learn.

Practice projects:

1. Build a simple model to predict a numeric value from sample data.
2. Create a basic classification model using a suitable public or sample dataset.
3. Compare model predictions with actual results.

These are introductory learning exercises. The results depend on the quality of the data, the chosen model, and how it is evaluated.

Module 6: Generative AI Fundamentals

Generative AI tools can produce new content based on the information and instructions they receive. They can support tasks involving text, code, images, and other content types.

Topics may include:

1. Introduction to generative AI.
2. Understanding large language models.
3. What prompts are and how they work.
4. Writing clear instructions for AI tools.
5. Improving prompts through testing.
6. Summarizing and organizing text.
7. Using AI to support coding practice.
8. Checking AI-generated answers.
9. Recognizing incorrect or unsupported output.
10. Privacy, copyright, and responsible use.

Practice projects:

1. Create a simple text summarizer workflow.
2. Build a prompt-based question-and-answer demo.
3. Use an AI tool to classify or organize sample text.

The specific tools and services used should be listed only after confirming what is included in the course.

Module 7: Connecting Python with AI Tools

Learners can explore how a Python application communicates with an AI service or model, if this module is part of the program.

Topics may include:

1. Understanding APIs.
2. Sending requests and receiving responses.
3. Working with JSON data.
4. Using environment variables for configuration.
5. Protecting API keys and other secrets.
6. Handling connection errors.
7. Managing unexpected responses.
8. Testing application output.
9. Understanding usage limits and costs.

Practice project: Build a basic Python application that sends a user question to a selected AI service and displays the response.

This project may require an internet connection, service credentials, and an API account. Availability and cost depend on the selected provider.

Module 8: Building Python with AI Projects

Projects help learners apply programming concepts to a complete task. The number and complexity of projects should match the course duration.

Suggested projects include:

1. AI-assisted FAQ application: Create a simple application that answers questions using an approved set of information and an appropriate AI tool.
2. Text summarizer: Build a demo that takes a block of text and produces a shorter version for review.
3. Data analysis assistant: Use Python to summarize a dataset and display selected statistics or charts. If an AI tool is included, use it to explain results while checking the output for accuracy.
4. Sentiment analysis demo: Explore how text can be classified into categories such as positive, negative, or neutral using a suitable method or model.
5. Document question-answering demo: Explore how an application can retrieve relevant information from a set of sample documents and provide a response.

These are suggested educational projects, not claims that every project is already part of the HSK SoftAcademy course