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Romeo Kienzler, Alex Aklson, Joseph Santarcangelo, Samaya Madhavan, and JEREMY NILMEIER
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...
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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.
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What's inside

Five courses

Introduction to Deep Learning & Neural Networks with Keras

(0 hours)
Looking to start a career in Deep Learning? This course will introduce you to the field and help you answer questions 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.

Building Deep Learning Models with TensorFlow

(0 hours)
The majority of data in the world is unlabeled and unstructured. 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.

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

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. Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner.

AI Capstone Project with Deep Learning

In this capstone, learners will apply their deep learning knowledge to a real world challenge, using 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 it, and present a project report to demonstrate the validity of their model and their proficiency in deep learning.

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