Professional Machine Learning with Python

Module 1: Introduction to Machine Learning

• Basics of machine learning.
• Types of machine learning (e.g., supervised, unsupervised).
• Machine learning applications.

Module 2: Supervised Learning

• Regression and classification algorithms.
• Model training and evaluation.
• Feature engineering.

Module 3: Unsupervised Learning

• Clustering and dimensionality reduction.
• K-means clustering, PCA, and more.
• Anomaly detection.

Module 4: Deep Learning and Neural Networks

• Neural network fundamentals.
• Building and training deep learning models (e.g., TensorFlow, Keras).
• Convolutional and recurrent neural networks.

Module 5: Machine Learning in Practice

• Real-world machine learning projects.
• Model deployment and integration.
• Model monitoring and maintenance.