Learn the crucial steps of cleaning, transforming, and structuring raw data into a usable format. Feature engineering enhances model performance by selecting the most relevant features and creating new ones from existing data.

Understand the difference between supervised learning, where models are trained on labeled data, and unsupervised learning, where algorithms identify patterns and structures in unlabeled data. You’ll work with algorithms like linear regression, decision trees, clustering, and more.

Dive into advanced topics like deep learning and neural networks, which mimic the human brain’s processing to solve complex problems. You’ll gain experience with popular frameworks like TensorFlow and Keras to build and train neural networks for tasks like image recognition and speech processing.

Learn how to assess the performance of machine learning models using various evaluation metrics (e.g., accuracy, precision, recall). You’ll also explore techniques for optimizing models, such as hyperparameter tuning and cross-validation, to improve their effectiveness.