IBM AI Engineering Professional Certificate

Create custom layers and models in Keras and integrate Keras with TensorFlow 2.x

Develop advanced convolutional neural networks (CNNs) using Keras

Develop Transformer models for sequential data and time series prediction

Explain key concepts of Unsupervised learning in Keras, Deep Q-networks (DQNs), and reinforcement learning

Job-ready PyTorch skills employers need in just 6 weeks

How to implement and train linear regression models from scratch using PyTorch’s functionalities

Key concepts of logistic regression and how to apply them to classification problems

How to handle data and train models using gradient descent for optimization 

Key concepts on Softmax regression and understand its application in multi-class classification problems.

How to develop and train shallow neural networks with various architectures.

Key concepts of deep neural networks, including techniques like dropout, weight initialization, and batch normalization.

How to develop convolutional neural networks, apply layers and activation functions.

Build a deep learning model to solve a real problem.

Execute the process of creating a deep learning pipeline.

Apply knowledge of deep learning to improve models using real data.

Demonstrate ability to present and communicate outcomes of deep learning projects.

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.