IBM AI Engineering 專業證書
Launch your career as an AI engineer. Learn how to provide business insights from big data using machine learning and deep learning techniques.
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.
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？
What are some examples of careers in artificial intelligence?
An understanding of artificial intelligence can be used to support many careers, but some careers specifically require a background in AI. Some examples of careers in AI include:
- AI Developer
- Data Analyst
- Data Engineer
- Data Scientist
- Machine Learning Engineer
- Marketing Analyst
- Operations Analyst
- Quantitative Analyst
- Software Analyst
- Software Developer
- Software Engineer
- User Experience Engineer
How long does it take to complete the Professional Certificate?
This Professional Certificate consists of 6 self-paced courses. Each course takes 4-5 weeks to complete if you spend 2-4 hours working through the course per week. At this rate, the entire Professional Certificate can be completed in 3-6 months. However, you are welcome to complete the program more quickly or more slowly, depending on your preference.
What background knowledge is necessary?
This Professional Certificate's pre-requisites includes the following skills:
- Working knowledge of Python and Jupyter Notebooks (Don't have these skills? Try taking the Python for Data Science and course)
- 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 Professional Certificate?
At this time there is no university credit for completing courses in this program.
What will I be able to do upon completing the Professional Certificate?
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