Mastering Generative AI: Model Foundations and NLP from edX

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.

What's inside

Learning objectives

Job-ready skills in foundational generative ai and nlp techniques employers are looking for in just 2 weeks.
A working understanding of one-hot encoding, bag-of-words, embedding, and embedding bags to convert words to features.
Applied knowledge of word2vec models for contextual embedding.

How to create a simple language model with a neural network.
How to use n-gram and sequence-to-sequence models for document classification, text analysis, and sequence transformation… and more.

Job-ready skills in foundational generative ai and nlp techniques employers are looking for in just 2 weeks.
A working understanding of one-hot encoding, bag-of-words, embedding, and embedding bags to convert words to features.
Applied knowledge of word2vec models for contextual embedding.
How to create a simple language model with a neural network.
How to use n-gram and sequence-to-sequence models for document classification, text analysis, and sequence transformation… and more.

Syllabus

Module 1: Fundamentals of Language Understanding

Video: Course Introduction

Reading: Course Overview

Reading: Professional Certificate Overview

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides hands-on labs for practical experience in implementing document classification using torchtext in PyTorch, building, and training a simple language model with a neural network to generate text

Teaches skills in foundational generative AI and NLP techniques, which are highly sought after by employers looking to fill roles in the rapidly growing gen AI market

Explores document classification, language modeling, and language translation, which are essential for building small and large language models used in generative AI applications

Requires a basic knowledge of Python and familiarity with machine learning and neural network concepts, which may necessitate additional preparation for learners without this background

Presented by IBM, a company recognized for its contributions to artificial intelligence and natural language processing, which may add credibility to the course content

Covers N-gram language models and sequence-to-sequence models, which are fundamental concepts for document classification, text analysis, and sequence transformation in NLP

Reviews summary

Foundations in genai and nlp with pytorch

According to students, this course provides a solid foundation in the core concepts of generative AI and Natural Language Processing (NLP). Learners appreciated the coverage of techniques like word embeddings and language modeling, finding it highly relevant to career goals in AI and data science. The course leverages PyTorch for implementations, which many found valuable. While many praised the hands-on labs for practical experience, some noted that the stated prerequisites (basic Python, ML, NN) might be understated, finding the material challenging without a stronger background.

Introduces important NLP/GenAI models.

"The modules on Word2Vec and sequence-to-sequence models were particularly insightful."

"I appreciated learning about N-gram models and how they connect to neural networks."

"Get an introduction to classic NLP models and how they relate to modern GenAI."

"The course provides good coverage of fundamental models like Word2Vec and Seq2Seq."

Skills are valuable for AI careers.

"The skills taught here are exactly what I see required for entry-level AI/ML roles."

"This course directly contributes to skills needed for jobs in generative AI."

"Highly relevant content for anyone pursuing a career in data science or AI."

"I feel more confident discussing GenAI and NLP concepts in job interviews now."

Hands-on exercises are very helpful.

"The labs are really practical and helped solidify the theoretical concepts with actual coding examples."

"Working with PyTorch in the labs was a key benefit of this course."

"I especially enjoyed the lab on building a simple language model; it made everything click."

"The hands-on assignments were the best part, allowing me to apply what I learned immediately."

Provides a strong base in core concepts.

"This course gave me an excellent introduction to the fundamental concepts behind generative AI and NLP."

"I feel like I have a much better grasp on topics like word embeddings and language models after taking this course."

"It covers the essential building blocks needed to understand more advanced GenAI topics."

"A great starting point for understanding the core mechanics of NLP for AI."

More difficult than expected for some.

"While it says 'basic' prerequisites, I think you need a solid understanding of Python, ML, and neural networks to keep up."

"Found it quite challenging at times, probably because my background in neural networks wasn't as strong as needed."

"Beginners might struggle; it moves pretty fast assuming prior knowledge."

"If you don't have a firm grasp of PyTorch beforehand, prepare for a steep learning curve."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Mastering Generative AI: Model Foundations and NLP with these activities:

Review Neural Network Fundamentals

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Solidify your understanding of neural networks to better grasp the concepts of language models.

Browse courses on Neural Networks

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Review the basic architecture of neural networks.
Study activation functions and loss functions.
Practice backpropagation with simple examples.

Brush Up on Python and PyTorch

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Strengthen your Python and PyTorch skills to effectively implement and train generative AI models.

Browse courses on PyTorch

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Review Python syntax and data structures.
Practice PyTorch tensor operations and model building.
Work through PyTorch tutorials on basic neural networks.

Read 'Natural Language Processing with Python'

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Gain a solid foundation in NLP concepts and techniques using Python.

View Natural Language Processing with Python:... on Amazon

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Read the chapters on text processing and feature extraction.
Experiment with the NLTK library for text analysis.
Apply the learned techniques to a small text dataset.

Four other activities

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Implement One-Hot Encoding and Bag-of-Words

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Reinforce your understanding of feature engineering techniques for NLP.

Browse courses on One-Hot Encoding

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Implement one-hot encoding from scratch in Python.
Implement bag-of-words using scikit-learn.
Compare the performance of both techniques on a text classification task.

Create a Blog Post on Word Embeddings

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Deepen your understanding of word embeddings by explaining the concepts to others.

Browse courses on Word Embeddings

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Research different types of word embeddings (word2vec, GloVe, FastText).
Write a blog post explaining the intuition behind word embeddings.
Include code examples demonstrating how to use word embeddings in PyTorch.
Publish the blog post on a platform like Medium or your personal website.

Build a Simple Text Summarization Model

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Apply your knowledge of sequence-to-sequence models to build a practical NLP application.

Browse courses on Text Summarization

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Choose a dataset of text documents and their summaries.
Implement a sequence-to-sequence model with an encoder and decoder in PyTorch.
Train the model on the dataset.
Evaluate the model's performance using metrics like BLEU.

Read 'Speech and Language Processing'

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Gain a deeper understanding of the theoretical foundations of NLP.

View Melania on Amazon

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Read the chapters on language modeling and machine translation.
Study the mathematical formulations of NLP algorithms.
Implement some of the algorithms from scratch.

Career center

Learners who complete Mastering Generative AI: Model Foundations and NLP will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

A Natural Language Processing Engineer focuses on creating systems that can understand, interpret, and generate human language. This course directly aligns with this role by offering in-depth knowledge of how to implement, train, and evaluate generative AI models for NLP. The course teaches how to convert words to features through various techniques such as one-hot encoding and embedding, which are all critical for NLP. A Natural Language Processing Engineer applies these methods in daily work. The focus on document classification, language modeling, and language translation is also directly relevant to daily tasks. The practical implementation of PyTorch with word2vec models further enhances this course's relevance, making it ideal for someone seeking to excel in this field. A learner will also explore sequence to sequence models and will develop the ability to evaluate generated text.

See salaries and explore the career path for Natural Language Processing Engineer

Machine Learning Engineer

A Machine Learning Engineer builds and deploys machine learning models, and this course directly supports that work, especially for natural language processing tasks. The course emphasizes practical skills in creating and implementing models, like neural networks and sequence-to-sequence models, which are essential for an engineer's daily work. The course specifically addresses how to convert words to features using methods such as one-hot encoding and word2vec, which are vital for preparing data for machine learning algorithms. Furthermore, the hands-on labs, where document classification is implemented using torchtext in PyTorch, directly translate to the kind of hands-on coding ML engineers need to succeed. By engaging with this course, a future Machine Learning Engineer will gain familiarity with both fundamental and advanced techniques, and will directly improve their ability to develop intelligent systems that can understand and generate human language.

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Artificial Intelligence Specialist

An Artificial Intelligence Specialist uses machine learning, deep learning, and other methods to create and improve AI systems. This course will assist in making a candidate more competitive for such a specialized position by building technical skills directly applicable to the field. The course focuses on generative AI and NLP techniques which are a core part of the current AI landscape. An Artificial Intelligence Specialist will benefit from the course’s coverage of converting words to features, implementing various embedding techniques and building small and large language models. This course also allows practical experience with tools, like PyTorch, and will equip learners with the ability to build complex neural networks and sequence to sequence models, enhancing their competitiveness as AI specialists. The evaluation of text using metrics can assist with fine-tuning models.

See salaries and explore the career path for Artificial Intelligence Specialist

Data Scientist

A Data Scientist uses statistical and machine learning techniques to analyze data and extract meaning. This course is helpful for a data scientist, particularly one working with text data, because it provides a solid understanding of how to apply generative AI and NLP methods. The course teaches converting words to features, a necessary step in any text data project. A data scientist would benefit from learning methods such as one-hot encoding, bag of words, embedding, and embedding bags. The course also covers how to build and train language models. The hands-on labs, such as implementing document classification using torchtext in PyTorch, allow for practical application of the methods taught. These skills all contribute to a data scientist's ability to work with text data effectively and creatively.

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Research Scientist

A Research Scientist in artificial intelligence conducts research to develop new AI methods and models, often requiring a master's degree or a Ph.D. This course provides an excellent grounding in generative AI models and NLP, an important subfield of AI research. The course explores techniques such as one-hot encoding and word2vec models, which allow for the conversion of words into feature representations. The knowledge of building and training neural networks for document categorization, and of metrics to evaluate text, will help a research scientist develop their ability to interpret results. The course allows a researcher to develop skill in sequence-to-sequence models, which have broad application in AI research. The lessons learned prepare students to further explore the field of artificial intelligence.

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Computational Linguist

A Computational Linguist develops computational models of human language, often requiring an advanced degree. They will find this course particularly helpful because it dives into the practical implementations of NLP, and provides a detailed look into generative AI. A computational linguist often deals with language modeling, which this course covers in detail. The exploration of N-gram models, and sequence-to-sequence models, are directly relevant to the daily work of this role. Understanding how to convert words to features through methods like bag-of-words and word embeddings is another relevant skill. The practical focus of the course's labs, including using PyTorch for text analysis and model building, greatly enhances the benefits of this course for someone wishing to become a computational linguist.

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AI Product Manager

An AI Product Manager defines the vision and strategy for AI-powered products. This course may be useful to an AI product manager because it provides key technical insights related to the development of AI tools. The course's focus on generative AI and NLP can help Product Managers understand the capabilities and limitations of their products. An AI Product Manager will benefit from learning about language models, word embeddings, and sequence-to-sequence models. A high level understanding of the conversion of words to features can make collaboration with engineers more effective. Having familiarity with text analysis and training with neural networks can also inform strategic conversation about development. This course will allow an AI Product manager to discuss their products in more technical terms.

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Software Engineer

A Software Engineer develops and maintains software systems. While not a direct match, this course may be useful to a software engineer who wants to work with AI applications. The course provides practical skills in machine learning and natural language processing that are now increasingly relevant in software development. Specifically, the skills in converting words to features, building language models, and training neural networks, will assist a software engineer who wants to work on intelligent applications that are built using these techniques. This course can help a Software Engineer broaden their skills and open new possibilities by learning how to incorporate generative AI and NLP in their work. A Software Engineer can use these advanced skills when building applications related to text or audio.

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Data Analyst

A Data Analyst interprets data and creates visualizations. This course may be useful to a data analyst who wants to analyze and visualize textual data. The course will teach skills in natural language processing that can be applied to text analysis. For example, a data analyst would benefit from learning how to convert words to features using techniques such as bag of words and embedding. Familiarity with word2vec models can also refine an analyst's ability to generate useful insights from text. By learning document classification and language modeling, the data analyst can process text data with greater sophistication, opening up new possibilities for data analysis. The course will allow the data analyst to go beyond numbers and gain new insight from textual data.

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Technical Writer

A Technical Writer creates documentation for technical products. This course may be relevant for a technical writer who must document AI models or products using generative AI and NLP. The course’s content can help a technical writer produce accurate and informative documentation for AI tools by allowing a basic understanding of the technical components. Familiarity with language modeling and text generation can make the technical writer’s documentation more easily understood. A technical writer may also find it useful to understand concepts related to word embeddings and sequence to sequence models. Having hands-on experience from the course labs may translate to the ability to explain these technical components with more clarity.

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Content Creator

A Content Creator produces material for digital platforms. This course may be useful for a content creator who wants to explore the latest trends in AI and NLP, and potentially use these tools to enhance output. Because it focuses on text generation, the course may allow a content creator to refine their work or generate new ideas. A content creator may be interested in language models and text generation, both of which are covered in the course. This course’s focus in generating text and analyzing language is a possible expansion of a content creator skillset. This can make content creators aware of new trends in AI.

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Business Analyst

A Business Analyst identifies business needs and solutions. This course may be useful for a business analyst who wants to understand more about AI and its applications to business. By learning more about these technical components of AI, the business analyst could recommend better solutions for the company. The course focuses on generative AI and natural language processing, a field that has direct applications to many business problems. By understanding document classification and text generation, a business analyst might be better able to assess opportunities and provide recommendations. The business analyst can then use this knowledge to improve their recommendations.

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Project Manager

A Project Manager plans and executes projects, and may benefit from understanding the technical side of AI. While this is not a direct fit, the project manager is an important part of any team. The course may provide insight into the models and methods used in AI development. The project manager may benefit from learning how data is categorized and how language models work. Familiarity with the methods used to generate text may assist the project manager in setting accurate timelines and goals and communicating with engineers. A project manager who understands these methods within AI may be able to more effectively manage AI related projects.

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Technical Support Specialist

A Technical Support Specialist helps customers with technical issues. While this is not a direct fit, a technical support specialist who works with AI or text based applications may find the course useful in diagnosing problems. The course’s content relating to natural language processing could help a support specialist understand how AI can produce human language. By understanding document classification and text generation, a support specialist might be better able to troubleshoot customer issues. This course may provide a support specialist with the background necessary to understand the issues a customer is encountering.

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Educator

Educators design and deliver educational material. While this is not a direct fit, educators interested in current trends in AI may find the course useful. The course provides an overview of NLP techniques and tools. Educators may want to understand how text generation is used to promote learning and collaboration. Educators who want to stay current with changes in the field may find the course an interesting introduction to these topics, especially the methods underlying AI tools. The educator can then use the provided information to update their teaching material.

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Mastering Generative AI

Model Foundations and NLP

What's inside

Learning objectives

Syllabus

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Reviews summary

Foundations in genai and nlp with pytorch

Activities

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