Machine Learning Specialization

Course 1: Built supervised models for prediction and binary classification (linear and logistic regression) in Python with NumPy and scikit-learn; implemented gradient descent and optimization; evaluated and tuned models with real data.

Course 2: Built and trained neural networks in TensorFlow for multi-class classification; applied ML best practices so models generalize (evaluation, tuning, data-centric improvement); implemented decision trees, random forests, and boosted trees; touched transfer learning and data ethics.

Course 3: Clustering and anomaly detection; recommender systems with collaborative filtering and content-based deep learning; built a deep reinforcement learning model.

In practice: can ship regression/classification pipelines, design and train neural nets for classification, use tree ensembles for tabular data, build recommenders, and apply evaluation and tuning in production-style workflows.