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IBM AI Engineering Professional Certificate (V2)

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This badge earner has demonstrated proficiency in Machine Learning (ML) and Deep Learning (DL). The earner understands various ML techniques such as regression, classification, clustering & recommender systems and is able to use Python, Pillow, and OpenCV libraries to understand Computer Vision and its various applications across many industries, for image processing, performing image classification and object detection. The earner is able to build, test & deploy DL models using libraries such as Keras, PyTorch & TensorFlow. The earner has completed several ML & DL projects and is now armed with skills for starting a career in AI Engineering.

Skills

What it takes to earn this badge

Complete all courses in the IBM AI Engineering Professional Certificate program on Coursera (includes quizzes, hands-on as Neural Network assignments and projects), and earn the following badges:

1. Machine Learning with Python

2. Introduction to Deep Learning & Neural Networks with Keras

3. Introduction to Computer Vision and Image Processing

4. Deep Neural Networks with PyTorch

5. Building Deep Learning Models with TensorFlow

6. Successful completion of the AI Capstone Project with Deep Learning

7. Receive the AI Engineering Professional Certificate from Coursera

Endorsements

Credit 3 Universities DSc 2_edited_edite

About 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. 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.

AI Applied Capstone Project with Deep Learning

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.