ARTIFICIAL INTELLIGENCE ENGINEER 1
Completed by Mouza AlDarmaki on May 24, 2025
Verified learner ·
Badge ID: 0NJZ94iLTWKSWfeIi31izQ
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.