The IBM AI Engineering Professional Certificate is a comprehensive program designed for students and professionals aiming to build expertise in the rapidly evolving field of Artificial Intelligence (AI). Whether you are just beginning your AI journey or seeking to enhance your current skill set, this certification offers a robust foundation in essential AI and machine learning concepts.
Through hands-on projects, practical labs, and expert guidance, you will learn to develop, implement, and optimize AI solutions using industry-leading tools and techniques. Ideal for those looking to advance in AI engineering, this certificate equips you with the knowledge and practical experience required to succeed in today’s data-driven world. 
IBM AI Engineering Professional Certificate
This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer.
- Machine Learning with Python
- Introduction to Deep Learning and Neural Networks with Keras
- Introduction to Computer vision and Image Processing
- Deep Neural networks with PyTorch
- Building Deep Learning models with Tensorflow
- AI Capstone Project with Deep Learning
You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.
Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering.
Applied Learning Project
Throughout the program, you will build a portfolio of projects demonstrating your mastery of course topics. The hands-on projects will give you a practical working knowledge of Machine Learning libraries and Deep Learning frameworks such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow. You will also complete an in-depth Capstone Project, where you’ll apply your AI and Neural Network skills to a real-world challenge and demonstrate your ability to communicate project outcomes.
What You will learn from this IBM AI Engineering Professional Certificate:
- Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction
- Deploy machine learning algorithms and pipelines on Apache Spark
- Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn
- Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow
Skills you will learn from this IBM AI Engineering Professional Certificate Program:
- Image Processing
- AI
- OpenCV
- Computer vision
- Deep Learning
- ML
- Regression
- Hierarchical Clustering
- Classification
- SciPy and scikit-learn
- Artificial Neural Network
- Keras
- Reinforcement Learning
- Transformers
- Convolutional Neural networks CNN
- TensorFlow Keras
- Generative Adversarial Networks (GANs)
Pre-requisite Skills Required for this IBM AI Engineering Professional Certificate Program
- Working knowledge of Python and Jupyter Notebooks
- High school mathematics or math for machine learning
Key Things:
Upon completing this Professional Certificate you will be able to:
- Describe what machine learning (ML), deep learning (DL), and neural networks are
- Explain ML algorithms including classification, regression, clustering, and dimensional reduction
- Implement supervised and unsupervised ML models using Scipy and Scikitlearn
- Express how Apache Spark works and how to perform machine learning on big data
- Deploy ML algorithms and pipelines on Apache Spark
- Demonstrate an understanding of deep learning models such as autoencoders, restricted Boltzmann machines, convolutional networks, recursive neural networks, and recurrent networks
- Build deep learning models and neural networks using the Keras library
- Utilize the PyTorch library for deep learning applications and build deep neural networks
- Explain foundational TensorFlow concepts like main functions, operations & execution pipelines
- Apply deep learning using TensorFlow and perform back propagation to tune the weights and biases
- Determine what kind of deep learning method to use in which situation and build a deep learning model to solve a real problem
- Demonstrate ability to present and communicate outcomes of deep learning projects
the IBM AI Engineering Professional Certificate serves as a valuable pathway for students and professionals eager to excel in the AI field. With a strong emphasis on practical skills and real-world applications, this program ensures that you are well-prepared to tackle AI challenges and leverage cutting-edge technologies. By completing this certification, you will not only enhance your technical abilities but also position yourself for rewarding career opportunities in AI engineering. Whether you’re looking to enter the AI workforce or advance in your current role, this certification provides the expertise and credibility needed to succeed in an AI-driven future.
