IBM
IBM AI Engineering Professional Certificate
IBM

IBM AI Engineering Professional Certificate

Get job-ready as an AI engineer . Build the AI engineering skills and practical experience you need to catch the eye of an employer in less than 3 months. Power up your resume!

Sina Nazeri
Fateme Akbari
Wojciech 'Victor' Fulmyk

Instructors: Sina Nazeri

109,094 already enrolled

Included with Coursera Plus

Earn a career credential that demonstrates your expertise
4.5

(6,826 reviews)

Intermediate level

Recommended experience

Flexible schedule
4 months, 10 hours a week
Learn at your own pace
Build toward a degree
Earn a career credential that demonstrates your expertise
4.5

(6,826 reviews)

Intermediate level

Recommended experience

Flexible schedule
4 months, 10 hours a week
Learn at your own pace
Build toward a degree

What you'll learn

  • Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction 

  • Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn 

  • Deploy machine learning algorithms and pipelines on Apache Spark 

  • Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow 

Details to know

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Taught in English
Recently updated!

October 2024

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  • Receive professional-level training from IBM
  • Demonstrate your technical proficiency
  • Earn an employer-recognized certificate from IBM
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Get exclusive access to career resources upon completion

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    Improve your resume and LinkedIn with personalized feedback

  • Interview prep

    Practice your skills with interactive tools and mock interviews

  • Career support

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Professional Certificate - 13 course series

Machine Learning with Python

Course 113 hours4.7 (16,465 ratings)

What you'll learn

  • 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 

Skills you'll gain

Category: Reinforcement Learning
Category: Transformers
Category: Convolutional Neural networks CNN
Category: TensorFlow Keras
Category: Generative Adversarial Networks (GANs)

Introduction to Deep Learning & Neural Networks with Keras

Course 28 hours4.7 (1,645 ratings)

What you'll learn

Skills you'll gain

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

Deep Learning with Keras and Tensorflow

Course 323 hours4.4 (865 ratings)

What you'll learn

  • 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

Skills you'll gain

Category: Softmax regression
Category: Neural Networks
Category: Activation functions
Category: PyTorch
Category: Convolutional Neural Networks

Introduction to Neural Networks and PyTorch

Course 417 hours4.4 (1,733 ratings)

What you'll learn

  • 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 

Skills you'll gain

Category: Artificial Intelligence (AI)
Category: Artificial Neural Network
Category: Machine Learning
Category: Deep Learning
Category: keras

Deep Learning with PyTorch

Course 520 hours

What you'll learn

  • 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.

Skills you'll gain

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

AI Capstone Project with Deep Learning

Course 616 hours4.5 (590 ratings)

What you'll learn

  • 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.

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

  • 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: Machine Learning
Category: regression
Category: Hierarchical Clustering
Category: classification
Category: SciPy and scikit-learn

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

Generative AI Language Modeling with Transformers

Course 98 hours4.5 (21 ratings)

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: Bidirectional Representation for Transformers (BERT)
Category: Positional encoding and masking
Category: Generative pre-trained transformers (GPT)
Category: Language transformation
Category: PyTorch functions

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

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: Logistic Regression
Category: PyTorch (Machine Learning Library)
Category: Gradient Descent
Category: Linear Regression
Category: TensorFlow

Fundamentals of AI Agents Using RAG and LangChain

Course 126 hours4.7 (16 ratings)

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.

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 Courses11,841 learners
Fateme Akbari
IBM
4 Courses4,639 learners
Wojciech 'Victor' Fulmyk
IBM
4 Courses35,429 learners

Offered by

IBM

Build toward a degree

When you complete this Professional Certificate, you may be able to have your learning recognized for credit if you are admitted and enroll in one of the following online degree programs.¹

Why people choose Coursera for their career

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"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

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Frequently asked questions

¹Based on Coursera learner outcome survey responses, United States, 2021.