Course on IBM AI Engineering Professional Certificate [Online, 8 Months]: Register Now

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About the course

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.

There are 6 Courses in this Professional Certificate
  • Machine Learning with Python: This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.
  • Scalable Machine Learning on Big Data using Apache Spark: This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer.
  • Introduction to Deep Learning & Neural Networks with Keras: Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.
  • Deep Neural Networks with PyTorch: The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch’s tensors and Automatic differentiation package.
  • Building Deep Learning Models with TensorFlow: The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems.
  • AI Capstone Project with Deep Learning: In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model.
Instructor
  • Saeed Aghabozorgi (Ph.D., Sr. Data Scientist)
  • Joseph Santarcangelo (Ph.D., Data Scientist at IBM)
  • Romeo Kienzler (Chief Data Scientist, Course Lead)
  • Alex Aklson (Ph.D., Data Scientist)

To enroll in this course, click the link below.

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Disclaimer : We try to ensure that the information we post on Noticebard.com is accurate. However, despite our best efforts, some of the content may contain errors. You can trust us, but please conduct your own checks too.

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