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.

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 models, AI technologies, and AI platforms such as IBM watsonx and Hugging Face.

Integrate and enhance large language models (LLMs) using RAG technology to infuse intelligence into apps and chatbots.

Utilize Python libraries like Flask and Gradio to create web applications that interact with generative AI models.

Build generative AI-powered applications and chatbots using generative AI models, Python, and related frameworks.

Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data

Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy

Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines

Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

Describe the various types of Machine Learning algorithms and when to use them 

Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression 

Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees 

Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics 

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 used in language processing.

Implement tokenization to preprocess raw textual data using NLP libraries such as NLTK, spaCy, BertTokenizer, and XLNetTokenizer.

Create an NLP data loader using PyTorch to perform tokenization, numericalization, and padding of text data.

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.