MACHINE LEARNING - L1
Completed by Rahma Mahmoud Hassan Mohamed on May 29, 2026
Verified learner ·
Badge ID: INvwLNwuQCOb8CzcLkAjag
About this Badge
The badge earner has demonstrated proficiency in machine learning by mastering fundamental concepts, including supervised and unsupervised learning, neural networks, and model evaluation. They have gained hands-on experience building machine learning models in Python using NumPy, scikit-learn, TensorFlow, and advanced techniques such as decision trees, recommender systems, and deep reinforcement learning. Through applied projects, they have developed the skills to apply machine learning solution
Learning objectives
- Build and train supervised machine learning models for regression and classification using Python libraries like NumPy and scikit-learn.
- Develop and implement neural networks and decision trees using TensorFlow for multi-class classification and tree ensemble methods.
- Apply unsupervised learning techniques such as clustering and anomaly detection, and construct recommender systems using collaborative filtering and deep learning methods.
Skills learned
- Machine Learning Algorithms
- Artificial Intelligence and Machine Learning (AI/ML)
- Random Forest Algorithm
- Artificial Neural Networks
- Decision Tree Learning
- Anomaly Detection
- Dimensionality Reduction
- Artificial Intelligence
- Reinforcement Learning
- Predictive Modeling
- Supervised Learning
- Performance Tuning
- Deep Learning
- Unsupervised Learning
- Python Programming
- Regression Analysis
- Statistical Modeling
- Jupyter
- Tensorflow
- Feature Engineering
- NumPy
- Data Ethics
- Machine Learning
- Algorithms
- Scikit Learn (Machine Learning Library)
- Classification And Regression Tree (CART)
- Data-Driven Decision-Making
- Skills section collapsed. Showing 6 of 27 skills.
Learning content completed
Some of the content and skills presented have been contributed by the content provider.