AI Programming in Python

Course Description

• Beginners: Individuals with some prior coding experience (in any language) who want to pivot their research towards python and AI.
• Domain Experts: Professionals who wish to learn the basics of how to apply python based AI tools to their existing datasets.
• Academic Researchers: Graduate students, postdoctoral fellows, ... wanting to involve AI programming in their domain studies.
• R/Matlab and Other Language Users: Programmers familiar with other languages who want to start to pivot to the Python ecosystem for deep learning frameworks.
• It is not designed as an advanced AI class for those already familiar with python and/or deep learning frameworks.
• It is not a class purely using prompts in enterprise models for productivity.

1. Get used to a python integrated development environment and Jupyter notebooks: Users will learn to  enage with a cloud or local development environment and utilize Jupyter Notebooks for interactive coding.
2. Master Core Syntax: Write Python code utilizing variables, data structures (lists, dictionaries, sets), loops, and control flow.
3. Implement Modular Code: Define functions and classes to write reusable, clean code.
4. Manipulate Data: Programmatically load, clean, and manipulate large datasets using Pandas.
5. Visualize Data: Create data visualizations using Matplotlib and Seaborn to understand data distributions before modeling.
6. Basic pytorch: Learners will use basic pytorch to fit neural networks from scratch, including specialized frameworks like convolutional neural networks.
7. Enterprise models: Learners will become efficient at programmatically using enterprise AI models with application program interfaces.

• Variables and Data Types 
• Lists, Dictionaries, Sets
• Control Flow (If/Else statements)
• Loops and Iteration (For and While loops)
• Function definitions (def), arguments, and return values
• Importing standard libraries

• Loading data with Pandas
• Data cleaning 
• DataFrame filtering and manipulation
• Introduction to PyTorch

• Structure of a neuron in a neural network
• Layers
• Activation Functions 
• Loss Functions 
• Optimizers 
• The Training Loop 

• Transformer architecture concepts (Attention mechanisms)
• Tokenization and embeddings
• Context windows and sequence limits
• Loading and running open-source and enterprise models
• Fine-tuning techniques on custom data

Certificate

After successfully completing the course, learners will receive a certificate of competency from the Johns Hopkins Bloomberg School of Public Health.

Learning Prerequisites

There are no specific course prerequisites. You will need to have English language fluency.

Technical Prerequisites

The course will take place on the Canvas LMS (Canvas.jhu.edu). You will need the ability to log on to the platform, preferably using Google Chrome, to complete the content, which will include videos, readings, and assignments. You may also need to take part in other activities on Canvas.jhu.edu.

• Hardware requirements: computer; webcam; microphone and speakers or headset.
• Software requirements: Google Chrome web browser; access to Canvas.jhu.edu; Zoom web conferencing; Microsoft Office or similar; email

Course Lifecycle and Access

After registering, you will receive an email confirming your registration. You will be granted access to the course, which includes complete course information. Please check the course listing for additional information on course access dates. For synchronous courses, learners will have access to the Canvas course for the duration of the course. For asynchronous courses, learners will have access for 180 days after registration. 

Tuition Remission

This course may qualify for tuition remission. Please check https://hr.jhu.edu/benefits-worklife/tuition-assistance/tuition-reimbursement/noncredit-courses-at-jhu/ for guidelines. If you are interested in using tuition remission, please email the OOE support email with your request.

Refund Policy and Timeline

• Synchronous Courses
  • Refunds may be requested up to the start of the live activity, unless otherwise noted. All refund requests will incur a 10% processing fee.
• Asynchronous Courses
  • Refunds are not allowed for active asynchronous courses.

Technical Support Statement

Please direct all technical support requests to the OOE support email. We will respond to requests within 24-48 hours. Requests received on weekends, overnights and holidays will be responded to on the next business day.

Accessibility Statement

JHU is committed to providing all participants with the opportunity to pursue excellence in their learning endeavors. This includes providing disability related accommodations and services in accordance with the Americans with Disabilities Act (ADA) and our goal of delivering accessible, inclusive experiences. If you require accommodations to complete this course, please reach out to the OOE support email. Please do not include any personal identifiable information such as social security number in any messages. 

Accreditation

JHU is not approved by Middle States for direct assessment credentials for academic credit. This course results in a certificate of completion. It will not show on any JHU transcripts and does not count toward an academic record.