ARTIFICIAL INTELLIGENCE ENGINEER 1
Completed by Yosef Ahmed Faisal on May 13, 2026
Verified learner ·
Badge ID: Hcy_fc-fRNGMv33Pn2TR-A
About this Badge
The badge earner has demonstrated foundational proficiency in AI engineering by mastering machine learning and deep learning fundamentals, including supervised and unsupervised learning. They have gained hands-on experience using Python and popular libraries to apply AI techniques to real-world problems in object recognition, computer vision, and natural language processing.
Learning objectives
- Develop and train deep learning models using Keras and TensorFlow, including CNNs, transformer models, and deep Q-networks.
- Apply machine learning techniques using Python libraries such as Scikit-learn, Matplotlib, and Pandas for supervised and unsupervised learning.
- Design, train, and optimize neural networks with PyTorch, focusing on tasks like image recognition and natural language processing.
Skills learned
- Artificial Intelligence and Machine Learning (AI/ML)
- Machine Learning Algorithms
- Applied Machine Learning
- Statistical Machine Learning
- Random Forest Algorithm
- Time Series Analysis and Forecasting
- Artificial Neural Networks
- Natural Language Processing
- Dimensionality Reduction
- Probability Distribution
- Artificial Intelligence
- Generative AI
- Statistical Methods
- Reinforcement Learning
- Computer Vision
- Predictive Modeling
- Supervised Learning
- Performance Tuning
- Deep Learning
- Unsupervised Learning
- Python Programming
- Regression Analysis
- Statistical Modeling
- Jupyter
- Tensorflow
- Feature Engineering
- Machine Learning
- Data Processing
- Data Manipulation
- Matplotlib
- Scikit Learn (Machine Learning Library)
- PyTorch (Machine Learning Library)
- Classification And Regression Tree (CART)
- Keras (Neural Network Library)
- Skills section collapsed. Showing 5 of 34 skills.
Learning content completed
Some of the content and skills presented have been contributed by the content provider.