The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning.
You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer.
By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer. You will also complete a Capstone Project and demonstrate ability to present and communicate outcomes of deep learning projects
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Can I just enroll in a single course?
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Certificate, you’re automatically subscribed to the full Certificate. Visit your learner dashboard to track your progress.
Is this course really 100% online? Do I need to attend any classes in person?
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
How long does it take to complete the Specialization?
This Professional Certificate consists of 6 self-paced courses. Effort required to complete each course is 4-5 weeks if spending 2-4 hours per week. At this rate the entire specialization can be completed in 3-6 months.
What background knowledge is necessary?
This Professional Certificate pre-requisties the following skills:
Working Knowledge of Python Programming language and Jupyter Notebooks e.g. Python for Data Science and AI
High School Mathematics or Math for Machine Learning
It is highly recommended that you complete either or both of the following Professional Certificates before starting this one:
Do I need to take the courses in a specific order?
It is highly recommended to complete the courses in the suggested order.
Will I earn university credit for completing the Specialization?
At this time there is no university credit for completing courses in this specialization.
What will I be able to do upon completing the Specialization?
Upon completing this Professional Certificate you will be able to:
Describe what is Machine Learning (ML), Deep Learning (DL) & Neural Networks
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 backpropagation 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