IBM AI ENGINEERING PROFESSIONAL CERTIFICATE
Course Certificates Completed
AI Capstone Project with Deep Learning
Machine Learning with Python
Scalable Machine Learning on Big Data using Apache Spark
Deep Neural Networks with PyTorch
Building Deep Learning Models with TensorFlow
Introduction to Deep Learning & Neural Networks with Keras
Knowledge of Machine Learning (ML) and Deep Learning (DL). The grantee understands various machine learning techniques such as regression, classification, clustering, and recommender systems, and can scale big data machine learning with Apache Spark. Earned money can create, test, and implement DL models using libraries like Keras, PyTorch, and Tensorflow. The employee has completed several ML and DL projects and now has the skills to start a career in AI Engineering.
WHAT YOU WILL 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
About this Professional Certificate
Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses. 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.
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.
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 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.