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 4 months. Power up your resume!

Sina Nazeri
Fateme Akbari
Wojciech 'Victor' Fulmyk

Instructors: Sina Nazeri

Sponsored by Coursera Learning Team

111,372 already enrolled

Earn a career credential that demonstrates your expertise
4.5

(6,866 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

(6,866 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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Advance your career with in-demand skills

  • Receive professional-level training from IBM
  • Demonstrate your technical proficiency
  • Earn an employer-recognized certificate from IBM
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Professional Certificate - 13 course series

Machine Learning with Python

Course 113 hours4.7 (16,540 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: Machine Learning

Introduction to Deep Learning & Neural Networks with Keras

Course 28 hours4.7 (1,657 ratings)

What you'll learn

Skills you'll gain

Category: Algorithms
Category: Artificial Neural Networks
Category: Deep Learning
Category: Human Learning
Category: Machine Learning
Category: Machine Learning Algorithms
Category: Network Model
Category: Applied Machine Learning
Category: Network Architecture
Category: Python Programming

Deep Learning with Keras and Tensorflow

Course 323 hours4.4 (871 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

Introduction to Neural Networks and PyTorch

Course 417 hours4.4 (1,738 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: Human Learning
Category: Machine Learning
Category: Deep Learning
Category: Python Programming
Category: Artificial Neural Networks
Category: Machine Learning Algorithms
Category: Applied Machine Learning
Category: Algorithms
Category: Regression
Category: Mathematics

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.

AI Capstone Project with Deep Learning

Course 616 hours4.5 (594 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: Deep Learning
Category: Machine Learning
Category: Python Programming
Category: Artificial Neural Networks
Category: Machine Learning Algorithms
Category: Applied Machine Learning
Category: Data Analysis
Category: Data Visualization
Category: Computer Programming

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.

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.

Generative AI Language Modeling with Transformers

Course 98 hours4.5 (26 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.

Generative AI Engineering and Fine-Tuning Transformers

Course 108 hours4.8 (13 ratings)

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.

Generative AI Advance Fine-Tuning for LLMs

Course 118 hours4.4 (13 ratings)

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

Fundamentals of AI Agents Using RAG and LangChain

Course 126 hours4.6 (21 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.

Instructors

Sina Nazeri
IBM
2 Courses13,151 learners
Fateme Akbari
IBM
4 Courses5,480 learners
Wojciech 'Victor' Fulmyk
IBM
4 Courses36,761 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

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¹Career improvement (i.e. promotion, raise) based on Coursera learner outcome survey responses, United States, 2021.