Explain the fundamental concepts and applications of AI in various domains.

Describe the core principles of machine learning, deep learning, and neural networks, and apply them to real-world scenarios.

Analyze the role of generative AI in transforming business operations, identifying opportunities for innovation and process improvement.

Design a generative AI solution for an organizational challenge, integrating ethical considerations.

Describe generative AI and distinguish it from discriminative AI.

Describe the capabilities of generative AI and its use cases in the real world.

Identify the applications of generative AI in different sectors and industries.

Explore common generative AI models and tools for text, code, image, audio, and video generation.

Explain the concept and relevance of prompt engineering in generative AI models.

Apply best practices for creating prompts and explore examples of impactful prompts.

Practice common prompt engineering techniques and approaches for writing effective prompts.

Explore commonly used tools for prompt engineering to aid with prompt engineering.

Describe the Web Application Development Ecosystem and terminology like front-end developer, back-end, server-side, and full stack.

Identify the developer tools, online editors like JSFiddle, and integrated development environments (IDEs) for building and testing web applications.

Create and structure basic web pages using HTML and style them with CSS.

Develop dynamic and interactive web pages using JavaScript, including DOM manipulation, form validation, and client-side scripting techniques.

Learn Python - the most popular programming language and for Data Science and Software Development.

Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.

Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.

Access and web scrape data using APIs and Python libraries like Beautiful Soup.

Describe the steps and processes involved in creating a Python application including the application development lifecycle

Create Python modules, run unit tests, and package applications while ensuring the PEP8 coding best practices

Explain the features of Flask and deploy applications on the web using the Flask framework

Create and deploy an AI-based application onto a web server using IBM Watson AI Libraries and Flask

Explain the core concepts of generative AI, including large language models, speech technologies, and platforms such as IBM watsonX, and Hugging Face

Build generative AI-powered applications and chatbots using LLMs, retrieval-augmented generation(RAG), and foundational Python frameworks

Integrate speech-to-text (STT) and text-to-speech (TTS) technologies to enable voice interfaces in generative AI applications

Develop web-based AI applications using Python libraries, such as Flask and Gradio, along with basic front-end tools like HTML, CSS, and JavaScript

Discuss how to generate code snippets, scripts, and programs using generative AI models

Apply techniques for application design and architecture, bug detection, code refactoring, and program optimization using generative AI

Develop innovative solutions using Gen AI-powered tools and models such as ChatGPT, GitHub CoPilot, Google Gemini, and IBM watsonx Code Assistant

Explain challenges and ethical considerations associated with using Generative AI for programming and options for mitigating them

Describe the role of a software engineer and some career path options as well as the prospective opportunities in the field.

Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.

Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.

Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.