Artificial Intelligence & Data Science
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IBM AI Engineering Professional Certificate

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Course Features

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Duration

9 months

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Effort

3 hours per week

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Teaching Type

Self Paced

Course Description

Artificial intelligence (AI), which is revolutionizing entire industries and changing the way businesses use data to make business decisions, has changed how companies across all sectors leverage data. Organizations need AI engineers with cutting-edge skills such as deep learning neural networks and machine learning algorithms to deliver data-driven actionable intelligence that will help them stay competitive. This 6-course Professional Certificate will equip you with all the necessary tools to excel in your career as an AI/ML engineer. With Python programming languages, you will learn the fundamentals of machine learning and deeplearning. Popular machine learning and deep-learning libraries like SciPy and ScikitLearn, Keras and PyTorch will be applied to solve industry problems such as image processing, object recognition, text analytics (NLP), recommender system, and other types classifiers. You'll learn the skills necessary to scale machine learning algorithms using Apache Spark through hands-on projects. You will learn how to build, train and deploy various deep architectures including convolutional neural networks and recurrent networks. You will receive an IBM digital badge that recognizes your expertise in AI engineering, along with a Coursera Professional Certificate.

Course Overview

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International Faculty

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Post Course Interactions

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Instructor-Moderated Discussions

Skills You Will Gain

What You Will Learn

Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow

Deploy machine learning algorithms and pipelines on Apache Spark

Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn

Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction

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