IBM AI Engineering 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.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency 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

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AI Capstone Project with Deep Learning (Coursera)

Nov 29th 2021
AI Capstone Project with Deep Learning (Coursera)
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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. They will load and pre-process data for a real problem, build the model and validate [...]
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Building Deep Learning Models with TensorFlow (Coursera)

Nov 29th 2021
Building Deep Learning Models with TensorFlow (Coursera)
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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 [...]
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Scalable Machine Learning on Big Data using Apache Spark (Coursera)

Nov 29th 2021
Scalable Machine Learning on Big Data using Apache Spark (Coursera)
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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. Apache [...]
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Introduction to Deep Learning & Neural Networks with Keras (Coursera)

Nov 29th 2021
Introduction to Deep Learning & Neural Networks with Keras (Coursera)
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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? [...]
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Machine Learning with Python (Coursera)

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will [...]
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Deep Neural Networks with PyTorch (Coursera)

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. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression.
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