IBM AI Engineering

IBM via Coursera Professional Certificate

Overview

AI is expected to grow 36.6% by 2030 (Forbes). This IBM AI Engineering Professional Certificate is ideal for data scientists, machine learning engineers, software engineers, and other technical specialists looking to get job-ready as an AI engineer. During this program, you’ll learn to build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, autoencoders,and generative AI models including large language models (LLMs). You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using Python. You’ll apply popular libraries such as SciPy, ScikitLearn, Keras, PyTorch, and TensorFlow to industry problems using object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), and recommender systems. Build Generative AI applications using LLMs and RAG with frameworks like Hugging Face and LangChain. You’ll work on labs and projects that will give you practical working knowledge of deep learning frameworks. If you’re looking to build job-ready skills and practical experience employers are looking for, ENROLL TODAY and build a resume and portfolio that stand out!

Syllabus

Course 1: Machine Learning with Python
- Offered by IBM. Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to ... Enroll for free.

Course 2: Introduction to Deep Learning & Neural Networks with Keras
- Offered by IBM. Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning ... Enroll for free.

Course 3: Deep Learning with Keras and Tensorflow
- Offered by IBM. Deep learning is revolutionizing many fields, including computer vision, natural language processing, and robotics. In ... Enroll for free.

Course 4: Introduction to Neural Networks and PyTorch
- Offered by IBM. PyTorch is one of the top 10 highest paid skills in tech (Indeed). As the use of PyTorch for neural networks rockets, ... Enroll for free.

Course 5: Deep Learning with PyTorch
- Offered by IBM. This course advances from fundamental machine learning concepts to more complex models and techniques in deep learning using ... Enroll for free.

Course 6: AI Capstone Project with Deep Learning
- Offered by IBM. In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use ... Enroll for free.

Course 7: Generative AI and LLMs: Architecture and Data Preparation
- Offered by IBM. This IBM short course, a part of Generative AI Engineering Essentials with LLMs Professional Certificate, will teach you the ... Enroll for free.

Course 8: Gen AI Foundational Models for NLP & Language Understanding
- Offered by IBM. This IBM course will teach you how to implement, train, and evaluate generative AI models for natural language processing ... Enroll for free.

Course 9: Generative AI Language Modeling with Transformers
- Offered by IBM. This course provides you with an overview of how to use transformer-based models for natural language processing (NLP). In ... Enroll for free.

Course 10: Generative AI Engineering and Fine-Tuning Transformers
- Offered by IBM. The demand for technical gen AI skills is exploding. Businesses are hunting hard for AI engineers who can work with large ... Enroll for free.

Course 11: Generative AI Advance Fine-Tuning for LLMs
- Offered by IBM. Fine-tuning a large language model (LLM) is crucial for aligning it with specific business needs, enhancing accuracy, and ... Enroll for free.

Course 12: Fundamentals of AI Agents Using RAG and LangChain
- Offered by IBM. Business demand for technical gen AI skills is exploding and AI engineers who can work with large language models (LLMs) are ... Enroll for free.

Course 13: Project: Generative AI Applications with RAG and LangChain
- Offered by IBM. Get ready to put all your gen AI engineering skills into practice! This guided project will test and apply the knowledge and ... Enroll for free.

Courses

4 reviews
13 hours 24 minutes
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Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning. This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms. By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.
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17 hours 14 minutes
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PyTorch is one of the top 10 highest paid skills in tech (Indeed). As the use of PyTorch for neural networks rockets, professionals with PyTorch skills are in high demand. This course is ideal for AI engineers looking to gain job-ready skills in PyTorch that will catch the eye of an employer. AI developers use PyTorch to design, train, and optimize neural networks to enable computers to perform tasks such as image recognition, natural language processing, and predictive analytics. During this course, you’ll learn about 2-D Tensors and derivatives in PyTorch. You’ll look at linear regression prediction and training and calculate loss using PyTorch. You’ll explore batch processing techniques for efficient model training, model parameters, calculating cost, and performing gradient descent in PyTorch. Plus, you’ll look at linear classifiers and logistic regression. Throughout, you’ll apply your new skills in hands-on labs, and at the end, you’ll complete a project you can talk about in interviews. If you’re an aspiring AI engineer with basic knowledge of Python and mathematical concepts, who wants to get hands-on with PyTorch, enroll today and get set to power your AI career forward!
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8 hours 21 minutes
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Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library. After completing this course, learners will be able to: • Describe what a neural network is, what a deep learning model is, and the difference between them. • Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines. • Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks. • Build deep learning models and networks using the Keras library.
0 reviews
16 hours 19 minutes
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In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.
0 reviews
23 hours 51 minutes
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Deep learning is revolutionizing many fields, including computer vision, natural language processing, and robotics. In addition, Keras, a high-level neural networks API written in Python, has become an essential part of TensorFlow, making deep learning accessible and straightforward. Mastering these techniques will open many opportunities in research and industry. You will learn to create custom layers and models in Keras and integrate Keras with TensorFlow 2.x for enhanced functionality. You will develop advanced convolutional neural networks (CNNs) using Keras. You will also build transformer models for sequential data and time series using TensorFlow with Keras. The course also covers the principles of unsupervised learning in Keras and TensorFlow for model optimization and custom training loops. Finally, you will develop and train deep Q-networks (DQNs) with Keras for reinforcement learning tasks (an overview of Generative Modeling and Reinforcement Learning is provided). You will be able to practice the concepts learned using hands-on labs in each lesson. A culminating final project in the last module will provide you an opportunity to apply your knowledge to build a Classification Model using transfer learning. This course is suitable for all aspiring AI engineers who want to learn TensorFlow and Keras. It requires a working knowledge of Python programming and basic mathematical concepts such as gradients and matrices, as well as fundamentals of Deep Learning using Keras.
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7 hours 57 minutes
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This IBM course will teach you how to implement, train, and evaluate generative AI models for natural language processing (NLP). The course will help you acquire knowledge of NLP applications including document classification, language modeling, language translation, and fundamentals for building small and large language models. You will learn about converting words to features. You will understand one-hot encoding, bag-of-words, embedding, and embedding bags. You also will learn how Word2Vec embedding models are used for feature representation in text data. You will implement these capabilities using PyTorch. The course will teach you how to build, train, and optimize neural networks for document categorization. In addition, you will learn about the N-gram language model and sequence-to-sequence models. This course will help you evaluate the quality of generated text using metrics, such as BLEU. You will practice what you learn using Hands-on Labs and perform tasks such as implementing document classification using torchtext in PyTorch. You will gain the skills to build and train a simple language model with a neural network to generate text and integrate pre-trained embedding models, such as word2vec, for text analysis and classification. In addition, you will apply your new skills to develop sequence-to-sequence models in PyTorch and perform tasks such as language translation.
0 reviews
5 hours 38 minutes
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This IBM short course, a part of Generative AI Engineering Essentials with LLMs Professional Certificate, will teach you the basics of using generative AI and Large Language Models (LLMs). This course is suitable for existing and aspiring data scientists, machine learning engineers, deep-learning engineers, and AI engineers. You will learn about the types of generative AI and its real-world applications. You will gain the knowledge to differentiate between various generative AI architectures and models, such as Recurrent Neural Networks (RNNs), Transformers, Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), and Diffusion Models. You will learn the differences in the training approaches used for each model. You will be able to explain the use of LLMs, such as Generative Pre-Trained Transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT). You will also learn about the tokenization process, tokenization methods, and the use of tokenizers for word-based, character-based, and subword-based tokenization. You will be able to explain how you can use data loaders for training generative AI models and list the PyTorch libraries for preparing and handling data within data loaders. The knowledge acquired will help you use the generative AI libraries in Hugging Face. It will also prepare you to implement tokenization and create an NLP data loader. 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.
0 reviews
8 hours 51 minutes
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This course provides you with an overview of how to use transformer-based models for natural language processing (NLP). In this course, you will learn to apply transformer-based models for text classification, focusing on the encoder component. You’ll learn about positional encoding, word embedding, and attention mechanisms in language transformers and their role in capturing contextual information and dependencies. Additionally, you will be introduced to multi-head attention and gain insights on decoder-based language modeling with generative pre-trained transformers (GPT) for language translation, training the models, and implementing them in PyTorch. Further, you’ll explore encoder-based models with bidirectional encoder representations from transformers (BERT) and train using masked language modeling (MLM) and next sentence prediction (NSP). Finally, you will apply transformers for translation by gaining insight into the transformer architecture and performing its PyTorch implementation. The course offers practical exposure with hands-on activities that enables you to apply your knowledge in real-world scenarios. This course is part of a specialized program tailored for individuals interested in Generative AI engineering. This course requires a working knowledge of Python, PyTorch, and machine learning.
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20 hours 30 minutes
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This course advances from fundamental machine learning concepts to more complex models and techniques in deep learning using PyTorch. This comprehensive course covers techniques such as Softmax regression, shallow and deep neural networks, and specialized architectures, such as convolutional neural networks. In this course, you will explore Softmax regression and understand its application in multi-class classification problems. You will learn to train a neural network model and explore Overfitting and Underfitting, multi-class neural networks, backpropagation, and vanishing gradient. You will implement Sigmoid, Tanh, and Relu activation functions in Pytorch. In addition, you will explore deep neural networks in Pytorch using nn Module list and convolution neural networks with multiple input and output channels. You will engage in hands-on exercises to understand and implement these advanced techniques effectively. In addition, at the end of the course, you will gain valuable experience in a final project on a convolutional neural network (CNN) using PyTorch. This course is suitable for all aspiring AI engineers who want to gain advanced knowledge on deep learning using PyTorch. It requires some basic knowledge of Python programming and basic mathematical concepts such as gradients and matrices.
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8 hours 2 minutes
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The demand for technical gen AI skills is exploding. Businesses are hunting hard for AI engineers who can work with large language models (LLMs). This Generative AI Engineering and Fine-Tuning Transformers course builds job-ready skills that will power your AI career forward. During this course, you’ll explore transformers, model frameworks, and platforms such as Hugging Face and PyTorch. You’ll begin with a general framework for optimizing LLMs and quickly move on to fine-tuning generative AI models. Plus, you’ll learn about parameter-efficient fine-tuning (PEFT), low-rank adaptation (LoRA), quantized low-rank adaptation (QLoRA), and prompting. Additionally, you’ll get valuable hands-on experience in online labs that you can talk about in interviews, including loading, pretraining, and fine-tuning models with Hugging Face and PyTorch. If you’re keen to take your AI career to the next level and boost your resume with in-demand gen AI competencies that catch the eye of an employer, ENROLL today and have job-ready skills you can use straight away within a week!
0 reviews
6 hours 59 minutes
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Business demand for technical gen AI skills is exploding and AI engineers who can work with large language models (LLMs) are in high demand. This Fundamentals of Building AI Agents using RAG and LangChain course builds job-ready skills that will fuel your AI career. During this course, you’ll explore retrieval-augmented generation (RAG), prompt engineering, and LangChain concepts. You’ll look at RAG, its applications, and its process, along with encoders, their tokenizers, and the FAISS library. Then, you’ll apply in-context learning and prompt engineering to design and refine prompts for accurate responses. Plus, you’ll explore LangChain tools, components, and chat models, and work with LangChain to simplify the application development process using LLMs. Additionally, you’ll get valuable 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 discuss in interviews. If you’re keen to boost your resume and extend your generative AI skills to applying transformer-based LLMs, ENROLL today and build job-ready skills in just 8 hours.
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8 hours 16 minutes
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Fine-tuning a large language model (LLM) is crucial for aligning it with specific business needs, enhancing accuracy, and optimizing its performance. In turn, this gives businesses precise, actionable insights that drive efficiency and innovation. This course gives aspiring gen AI engineers valuable fine-tuning skills employers are actively seeking. During this course, you’ll explore different approaches to fine-tuning and causal LLMs with human feedback and direct preference. You’ll look at LLMs as policies for probability distributions for generating responses and the concepts of instruction-tuning with Hugging Face. You’ll learn to calculate rewards using human feedback and reward modeling with Hugging Face. Plus, you’ll explore reinforcement learning from human feedback (RLHF), proximal policy optimization (PPO) and PPO Trainer, and optimal solutions for direct preference optimization (DPO) problems. As you learn, you’ll get valuable hands-on experience in online labs where you’ll work on reward modeling, PPO, and DPO. If you’re looking to add in-demand capabilities in fine-tuning LLMs to your resume, ENROLL TODAY and build the job-ready skills employers are looking for in just two weeks!
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9 hours 4 minutes
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Get ready to put all your gen AI engineering skills into practice! This guided project will test and apply the knowledge and understanding you’ve gained throughout the previous courses in the program. You will build your own real-world gen AI application. During this course, you will fill the final gaps in your knowledge to extend your understanding of document loaders from LangChain. You will then apply your new skills to uploading your own documents from various sources. Next, you will look at text-splitting strategies and use them to enhance model responsiveness. Then, you will use watsonx to embed documents, a vector database to store document embeddings, and LangChain to develop a retriever to fetch documents. As you work through your project, you will also implement RAG to improve retrieval, create a QA bot, and set up a simple Gradio interface to interact with your models. By the end of the course, you will have a hands-on project that provides engaging evidence of your generative AI engineering skills that you can talk about in interviews. If you’re ready to add some real-world experience to your portfolio, enroll today and fuel your AI engineering career.

Taught by

Alex Aklson, Ashutosh Sagar, Fateme Akbari, JEREMY NILMEIER, Joseph Santarcangelo, Kang Wang, Ricky Shi, Romeo Kienzler, Roodra Pratap Kanwar, SAEED AGHABOZORGI, Samaya Madhavan, Sina Nazeri and Wojciech 'Victor' Fulmyk