Last update: 16/11/2023
Attention to Visitors: Please note that this repository does not offer specific solutions for the courseworks. However, if you are seeking an overview of this certification, you are more than welcome to explore!
The IBM AI Engineering Professional Certification comprises 6 online courses designed to equip learners with the necessary tools and skills for success in the field of AI engineering. The program covers a wide range of topics including the fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. Applying 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.
This repository serves as a comprehensive collection of documentation and resources used throughout the certification journey. It includes relevant notes, code snippets, and other valuable materials that were utilized during the courses. Additionally, it provides proof of certification for each completed course, showcasing the learner's accomplishment and expertise in the field of AI engineering.
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✅ 02. Introduction to Deep Learning & Neural Networks with Keras
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✅ 06. AI Capstone Project with Deep Learning
✨✨Click on each course to view the relevant information and certificates!✨✨
The following tools were used to complete this certification:
The following Python libraries were used throughout the certification:
Rain Prediction in Australia with Machine Learning
Building a Regression Model with Keras
Image Classifier with ResNet18
