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, relevance, and best practices of prompt engineering to guide generative AI models in producing meaningful, accurate outputs.

Apply prompt engineering techniques to text prompts, improving the reliability and quality of large language models.

Practice prompt engineering techniques and approaches, including interview pattern, chain-of-thought, tree-of-thought, to improve prompt outcomes.

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

Develop a foundational understanding of Python programming by learning basic syntax, data types, expressions, variables, and string operations.

Apply Python programming logic using data structures, conditions and branching, loops, functions, exception handling, objects, and classes.

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

Access and extract web-based data by working with REST APIs using requests and performing web scraping with BeautifulSoup.

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

Build and deploy web applications using Flask, including routing, error handling, and CRUD operations.

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

Construct Python programs to clean and prepare data for analysis by addressing missing values, formatting inconsistencies, normalization, and binning

Analyze real-world datasets through exploratory data analysis (EDA) using libraries such as Pandas, NumPy, and SciPy to uncover patterns and insights

Apply data operation techniques using dataframes to organize, summarize, and interpret data distributions, correlation analysis, and data pipelines

Develop and evaluate regression models using Scikit-learn, and use these models to generate predictions and support data-driven decision-making

Job-ready foundational machine learning skills in Python in just 6 weeks, including how to utilizeScikit-learn to build, test, and evaluate models.

How to apply data preparation techniques and manage bias-variance tradeoffs to optimize model performance.

How to implement core machine learning algorithms, including linear regression, decision trees, and SVM, for classification and regression tasks.

How to evaluate model performance using metrics, cross-validation, and hyperparameter tuning to ensure accuracy and reliability.

Differentiate between generative AI architectures and models, such as RNNs, transformers, VAEs, GANs, and diffusion models

Describe how LLMs, such as GPT, BERT, BART, and T5, are applied in natural language processing tasks

Implement tokenization to preprocess raw text using NLP libraries like NLTK, spaCy, BertTokenizer, and XLNetTokenizer

Create an NLP data loader in PyTorch that handles tokenization, numericalization, and padding for text datasets

Explain how to use one-hot encoding, bag-of-words, embedding, and embedding bags to convert words to features.

Build and use word2vec models for contextual embedding.

Build and train a simple language model with a neural network.

Utilize N-gram and sequence-to-sequence models for document classification, text analysis, and sequence transformation.

Explain the concept of attention mechanisms in transformers, including their role in capturing contextual information.

Describe language modeling with the decoder-based GPT and encoder-based BERT.

Implement positional encoding, masking, attention mechanism, document classification, and create LLMs like GPT and BERT.

Use transformer-based models and PyTorch functions for text classification, language translation, and modeling.

Sought-after job-ready skills businesses need for working with transformer-based LLMs for generative AI engineering... in just 1 week.

How to perform parameter-efficient fine-tuning (PEFT) using LoRA and QLoRA

How to use pretrained transformers for language tasks and fine-tune them for specific tasks.

How to load models and their inferences and train models with Hugging Face.

In-demand gen AI engineering skills in fine-tuning LLMs employers are actively looking for in just 2 weeks

Instruction-tuning and reward modeling with the Hugging Face, plus LLMs as policies and RLHF

Direct preference optimization (DPO) with partition function and Hugging Face and how to create an optimal solution to a DPO problem

How to use proximal policy optimization (PPO) with Hugging Face to create a scoring function and perform dataset tokenization

In-demand job-ready skills businesses need for building AI agents using RAG and LangChain in just 8 hours.

How to apply the fundamentals of in-context learning and advanced methods of prompt engineering to enhance prompt design.

Key LangChain concepts, tools, components, chat models, chains, and agents.

How to apply RAG, PyTorch, Hugging Face, LLMs, and LangChain technologies to different applications.

Gain practical experience building your own real-world gen AI application that you can talk about in interviews.

Get hands-on using LangChain to load documents and apply text splitting techniques with RAG and LangChain to enhance model responsiveness.

Create and configure a vector database to store document embeddings and develop a retriever to fetch document segments based on queries.

Set up a simple Gradio interface for model interaction and construct a QA bot using LangChain and an LLM to answer questions from loaded documents.