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.
Can I just enroll in a single course?
此课程是 100% 在线学习吗？是否需要现场参加课程？
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.
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