IBM
Generative AI Engineering with LLMs Specialization
IBM

Generative AI Engineering with LLMs Specialization

Advance your ML career with Gen AI and LLMs . Master the essentials of Gen AI engineering and large language models (LLMs) in just 3 months.

Sina Nazeri
Fateme Akbari
Wojciech 'Victor' Fulmyk

Instructors: Sina Nazeri

Included with Coursera Plus

Get in-depth knowledge of a subject
Intermediate level

Recommended experience

3 months
at 4 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

3 months
at 4 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • In-demand, job-ready skills in gen AI, NLP apps, and large language models in just 3 months.

  • How to tokenize and load text data to train LLMs and deploy Skip-Gram, CBOW, Seq2Seq, RNN-based, and Transformer-based models with PyTorch

  • How to employ frameworks and pre-trained models such as LangChain and Llama for training, developing, fine-tuning, and deploying LLM applications.

  • How to implement a question-answering NLP system by preparing, developing, and deploying NLP applications using RAG.

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Taught in English
Recently updated!

September 2024

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Specialization - 7 course series

What you'll learn

  • 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.

Skills you'll gain

Category: Generative AI applications
Category: Retrieval augmented generation (RAG)
Category: Vector Database
Category: LangChain
Category: Gradio
Category: Vector database

What you'll learn

  • 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.

Skills you'll gain

Category: Retrieval augmented generation (RAG)
Category: In-context learning and prompt engineering
Category: LangChain
Category: Vector databases
Category: Chatbots

What you'll learn

  • 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.

Skills you'll gain

Category: Reinforcement Learning
Category: Proximal policy optimization (PPO)
Category: Reinforcement learning
Category: Direct preference optimization (DPO)
Category: Hugging Face
Category: Instruction-tuning

What you'll learn

  • 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.

Skills you'll gain

Category: Fine-tuning LLMs
Category: LoRA and QLoRA
Category: Pretraining transformers
Category: PyTorch
Category: Hugging Face

What you'll learn

  • 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

Skills you'll gain

Category: Positional encoding and masking
Category: Generative pre-trained transformers (GPT)
Category: Bidirectional encoder representations from transformers (BERT)
Category: Language transformation
Category: PyTorch functions

What you'll learn

  • 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.

Skills you'll gain

Category: N-Gram
Category: PyTorch torchtext
Category: Generative AI for NLP
Category: Word2Vec Model
Category: Sequence-to-Sequence Model

What you'll learn

  • 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.

Skills you'll gain

Category: Tokenization
Category: Hugging Face Libraries
Category: NLP Data Loader
Category: Large Language Models
Category: PyTorch

Instructors

Sina Nazeri
IBM
2 Courses7,621 learners
Fateme Akbari
IBM
4 Courses2,381 learners
Wojciech 'Victor' Fulmyk
IBM
3 Courses345 learners

Offered by

IBM

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