Generative AI Engineering

Please Note: Learners who successfully complete this IBM course can earn a skill badge —a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

Looking to kickstart a career in deep learning? Look no further. This course will introduce you to the field of deep learning and teach you the fundamentals. You will learn about some of the exciting applications of deep learning, the basics fo neural networks, different deep learning models, and how to build your first deep learning model using the easy yet powerful library Keras.

This course will presentsimplified explanations to some oftoday's hottest topics in data science, including:

• What is deep learning?
• How do neural networks learn and what are activation functions?
• What are deep learning libraries and how do they compare to one another?
• What are supervised and unsupervised deep learning models?
• How to use Keras to build, train, and test deep learning models?

The demand fordeep learning skills-- and the job salaries of deep learning practitioners -- arecontinuing to grow, as AI becomes more pervasive in our societies. This course will help you build the knowledge you need to future-proofyour career.

Please Note: Learners who successfully complete this IBM course can earn a skill badge —a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

Kickstart your learning of Python for data science, as well as programming in general with this introduction to Python course. This beginner-friendly Python course will quickly take you from zero to programming in Python in a matter of hours and give you a taste of how to start working with data in Python. ~~~~

Upon its completion, you'll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this course is for you.

You can start creating your own data science projects and collaborating with other data scientists using IBM Watson Studio. When you sign up, you will receive free access to Watson Studio. Start now and take advantage of this platform and learn the basics of programming, machine learning, and data visualization with this introductory course.

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

LEARN TO ANALYZE DATA WITH PYTHON

Learn how to analyze data using Python in this introductory course. You will go from understanding the basics of Python to exploring many different types of data through lecture, hands-on labs, and assignments. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

In this course, you will learn what AI is and understand its applications and use cases and how it is transforming our lives. You will explore basic AI concepts including machine learning, deep learning, and neural networks as well as use cases and applications of AI. You will be exposed to concerns surrounding AI, including ethics, bias, jobs and the impacts on society.

You will take a glimpse of the future with AI, get advice for starting an AI related career, and wrap up the course by demonstrating AI in action with a mini project.

This AI for Everyone course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

This course is the first part in a two part course and will teach you the fundamentals of Pytorch while providing the necessary prerequisites you need before you build deep learning models.

We will start off with PyTorch's tensors in one dimension and two dimensions , you will learn the tensor types an operations, PyTorchs Automatic Differentiation package and integration with Pandas and Numpy. This is followed by an in-depth overview of the dataset object and transformations; this is the first step in building Pipelines in PyTorch.

In module two we will learn how to train a linear regression model. You will review the fundamentals of training your model including concepts such as loss, cost and gradient descent. You will learn the fundamentals of PyTorch including how to make a prediction using PyTorch's linear class and custom modules. Then determine loss and cost with PyTorch. Finally you will implement gradient descent via first principles.

In module three you will train a linear regression model via PyTorch's build in functionality, developing an understanding of the key components of PyTorch. This will include how to effectively train PyTorch's custom modules using the optimizer object, allowing you an effective way to train any model. We will introduce the data loader allowing you more flexibility when working with massive datasets . You will learn to save your model and training in applications such as cross validation for hyperparameter selection, early stopping and checkpoints.

In module three you will learn how to extend your model to multiple input and output dimensions in applications such as multiple linear regression and multiple output linear regression. You will learn the fundamentals of the linear object, including how it interacts with data with different dimensions and number of samples. Finally you will learn how to train these models in PyTorch.

In module four you will review linear classifiers, logistic regression and the issue of using different loss functions. You will learn how to implement logistic regression in PyTorch several ways, including using custom modules and using the sequential method. You will test your skills in a final project.

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

In this mini-course you will demonstrate what you’ve learned in the Python Basics* course and apply your Python skills to a real-world project for your final assignment. You will be introduced to unit testing and will develop the skills needed to create functions and unit tests, run the unit tests, and package the files in a standard Python Package.

Hands-on labs provide practical application of the project work you’ll be performing in the Peer-graded Final Assignment.

At the end of this project, you will have the skills to test your Python code, build and run unit tests, and package the Python application for distribution.

PRE-REQUISITE: *Python Basics course from IBM is a pre-requisite for this project course. Please ensure before taking this course you have either completed the Python Basics course from IBM or have equivalent proficiency in working with Python and data.

NOTE: This course is designed for the learner to apply prior Python knowledge. It is not intended to teach you Python and includes minimal instructional content.

Prompt engineering is a process to effectively guide generative AI models and control their output to produce desired results. In this course, you will learn the techniques, approaches, and best practices for writing effective prompts.

You will learn about prompt techniques like zero-shot and few-shot, which can improve the reliability and quality of large language models (LLMs). You will also explore various prompt engineering approaches like Interview Pattern, Chain-of-Thought, and Tree-of-Thought, which aim at generating precise and relevant responses.

You will be introduced to commonly used prompt engineering tools like IBM watsonx Prompt Lab, Spellbook, and Dust.

The hands-on labs included in the course offer an opportunity to optimize results by creating effective prompts in the IBM Generative AI Classroom. You will also hear from practitioners about the tools and approaches used in prompt engineering and the art of writing effective prompts.

This course is designed for everyone, including professionals, executives, students, and enthusiasts interested in leveraging effective prompt engineering techniques to unlock the full potential of generative artificial intelligence (AI) tools like ChatGPT.

Generative AI modeling is an in-demand skill for AI model development. Employers now expect generative AI skills to be on an AI engineer’s resume. This hands-on course, which is also part of the IBM AI Applied Professional Certificate, will help you build the generative AI skills you need to stand out as an AI developer.

Throughout the course, you’ll get valuable practical experience working on guided projects that provide step-by-step instructions for building generative AI-powered applications. As part of this, you’ll work with Python and related libraries like Flask and Gradio, plus you’ll use frameworks such as Langchain. The course includes learning elements such as videos and readings to help you understand the models, frameworks, and technologies used in the projects.

You’ll also dive into building intelligent chatbots and apps using popular large language models (LLMs) such as GPT3 and Llama 2 hosted on platforms like IBM watsonx and Hugging Face. You'll explore retrieval-augmented generation (RAG) technology to enhance LLMs by incorporating external information beyond their training data. You’ll be able to build voice-enabled chatbots and apps using IBM Watson ® Speech Libraries for Embed.

To get the most out of this course, it is essential that you have a basic understanding of the Python programming language. It is also of benefit if you are familiar with HTML, CSS, and JavaScript, though this is not a requirement. This course is ideal for tech professionals who have some experience with Python and are ready to build the highly sought-after generative AI skills required to be an AI engineer or AI developer. If that’s you… enroll today and build job-ready gen AI skills in 6 weeks.

The gen AI market is projected to grow by 42% CAGR by 2033 (Bloomberg). And with natural language processing (NLP) being an integral part of this gen AI revolution, data scientists and AI professionals with the right skills are in high demand!

If you’re an aspiring AI professional or data scientist, this IBM course on Generative AI - Model Foundations and NLP gives you highly sought-after skills employers are looking for.

AI professionals use NLP to help generative AI applications understand and generate human language and enable tasks like text generation, summarization, translation, and conversational interactions.

During this course, you’ll learn how to implement, train, and evaluate gen AI models for NLP. You’ll explore document classification, language modeling, language translation, and develop a fundamental understanding of how to build small and large language models.

You’ll learn how to convert words to features. You’ll discover one-hot encoding, bag-of-words, embedding, and embedding bags. Plus, you’ll implement PyTorch to embed models using word2vec for feature representation in text data.

You’ll also build, train, and optimize neural networks for document categorization. You’ll learn about concepts such as N-gram language model and sequence-to-sequence models. And you’ll evaluate the quality of generated text using metrics, such as BLEU.

Importantly, you’ll get hands-on in labs, where you’ll gain practical experience in tasks such as implementing document classification using torchtext in PyTorch, and building and training a simple language model with a neural network to generate text. You’ll also integrate pre-trained embedding models, such as word2vec, for text analysis and classification.

If you’re an aspiring AI professional or data scientist looking to power up your resume with in-demand gen AI skillso, ENROLL TODAY and prepare to take your career to the next level!

Prerequisites: To enroll for this course, a basic knowledge of Python and familiarity with machine learning and neural network concepts is recommended.

The demand for gen AI is forecast to grow over 46% annually by 2030 (Source: Statista). AI engineers and developers, data scientists, machine learning engineers, and other AI professionals with gen AI skills are highly sought-after. This course builds in-demand skills in large language model (LLM) architecture and data preparation employers are looking for.

During the course, you’ll learn about real-world applications using generative AI. You’ll gain insights into gen AI architectures and models, such as recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. You’ll use different training approaches for each model. Plus, you’ll explore LLMs such as generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT).

Additionally, you’ll gain a detailed understanding of the tokenization process, tokenization methods, and the use of tokenizers for word-based, character-based, and subword-based tokenization. You’ll get hands-on experience using data loaders for training generative AI models, using PyTorch libraries, and generative AI libraries in Hugging Face. Plus, you’ll implement tokenization and create an NLP data loader.

If you’re looking to master gen AI LLM architecture and data preparation, ENROLL TODAY and get ready to power up your resume with skills employers need!

Prerequisites: To enroll for this course, a basic knowledge of Python and PyTorch and an awareness of machine learning and neural networks would be an advantage, though not strictly required.

The demand for transformer-based language models is skyrocketing. AI engineers skilled in using transformer-based models for NLP are essential for developing successful gen AI applications. This course builds the job-ready skills employers need.

During the course, you’ll explore the concepts of transformer-based models for natural language processing (NLP). You’ll look at how to apply transformer-based models for text classification, focusing on the encoder component. Plus, you’ll learn about positional encoding, word embedding, and attention mechanisms in language transformers, and their role in capturing contextual information and dependencies.

You’ll learn about multi-head attention and decoder-based language modeling with generative pre-trained transformers (GPT) for language translation. You’ll consider how to train models and implement models using PyTorch. You’ll explore encoder-based models with bidirectional encoder representations from transformers (BERT) and train them using masked language modeling (MLM) and next sentence prediction (NSP). Plus, you’ll learn to apply transformers for translation using transformer architecture and implement it using PyTorch.

Throughout, you’ll apply your new skills practically in hands-on activities and you’ll complete a final project tackling a real-world scenario.

If you’re looking to build job-ready skills for gen AI applications employers are looking for, ENROLL TODAY and enhance your resume in just 2 weeks!

Prerequisites: To enroll for this course, you need a working knowledge of Python, PyTorch, and machine learning.

The demand for technical gen AI skills is exploding. AI engineers who know how to fine-tune transformers for gen AI applications are in hot demand. This Generative AI Engineering Fine-Tuning with Transformers course is designed for AI engineers and other AI specialists who are looking to add highly sought-after skills to their resume.

In this course, you’ll explore the differences between PyTorch and Hugging Face. You’ll use pre-trained transformers for language tasks and fine-tune them for special tasks. Plus, you’ll fine-tune generative AI models using PyTorch and Hugging Face.

You’ll also explore concepts like parameter-efficient fine-tuning (PEFT), low-rank adaptation (LoRA), quantized low-rank adaptation (QloRA), model quantization with natural language processing (NLP) and prompting. Plus, through valuable hands-on labs, you’ll build your experience loading models and inference, training models with Hugging Face, pre-training LLMs, fine-tuning models, and PyTorch adaptors.

If you’re looking to gain the job-ready skills employers need for fine-tuning transformers for gen AI, ENROLL TODAY and power up your resume for career success!

Prerequisites: This course requires basic knowledge of Python, PyTorch, and transformer architecture. You should also be familiar with machine learning and neural network concepts.

Employers are actively hunting for AI engineers who know how to fine-tune transformers for gen AI applications. This Mastering Generative AI - Advanced Fine-Tuning for LLMs course is designed to give AI engineers and other AI specialists the highly sought-after skills employers need.

AI engineers use advanced fine-tuning skills for LLMs to tailor pre-trained models for specific tasks to ensure accuracy and relevance in applications like chatbots, translation, and content generation.

During this course, you’ll explore the basics of instruction-tuning with Hugging Face, reward modeling, and training a reward model. You’ll look at proximal policy optimization (PPO) with Hugging Face and its configuration, large language models (LLMs) as distributions, and reinforcement learning from human feedback (RLHF). Plus, you’ll investigate direct performance optimization (DPO) with Hugging Face using the partition function.

As you progress through the course, you’ll also build your practical hands-on experience in online labs where you’ll work on reward modeling, PPO, and DPO.

If you’re keen to extend your gen AI engineering skills to include advanced fine-tuning for LLMs so you can catch the eye of an employer, ENROLL TODAY and power up your resume in just 2 weeks!

Prerequisites: To take this course, you need knowledge of LLMs, instruction-tuning, and reinforcement learning. Familiarity with machine learning and neural network concepts is useful too.

The demand for technical gen AI skills is rocketing. AI engineers with competencies in large language models (LLMs), and related methodologies and frameworks such as RAG and LangChain, are highly sought-after. This Mastering Generative AI - Agents with RAG and LangChain course builds the job-ready skills you need to catch the eye of an employer.

During the course, you’ll explore retrieval-augmented generation (RAG), prompt engineering, and LangChain. You’ll build your understanding of RAG, its applications, and its process, along with encoders, their tokenizers, and the Facebook AI similarity search (FAISS) library. Further You’ll learn how to apply in-context learning and prompt engineering to design and refine prompts for accurate responses. Plus, you’ll dive into the world of LangChain tools, components, and chat models, and work with LangChain to simplify the application development process using LLMs.

Throughout the course, you’ll get hands-on practice in online labs developing applications using integrated LLM, LangChain, and RAG technologies. Plus, you’ll complete a real-world project you can talk about in interviews.

If you’re looking to build job-ready skills in RAG and LangChain that employers are looking for, ENROLL TODAY and get ready to power up your resume!