Machine Learning Fundamentals Journey
This path will introduce you to the core concepts and techniques of machine learning. You will learn about different types of ML, how to prepare data, build and evaluate models, and understand practical considerations for real-world applications.
What you'll achieve
Define machine learning and distinguish between its main types (supervised, unsupervised, reinforcement).
Explain the importance of data preprocessing and common techniques used.
Implement and interpret basic supervised learning models like linear and logistic regression.
Understand and apply fundamental unsupervised learning algorithms such as K-Means clustering.
Evaluate model performance using appropriate metrics and techniques like cross-validation.
Identify and address common challenges in ML, including overfitting and underfitting.
Describe the basics of neural networks and their role in deep learning.
Recognize ethical considerations and practical steps in deploying ML models.