Data Science & ML Essentials
Unlock the world of data science and machine learning. From exploring raw data to deploying intelligent models, this path covers the fundamental concepts and practical techniques you need to understand how AI works.
What you'll achieve
Explain the importance of Exploratory Data Analysis (EDA) and apply common techniques like visualization and statistical summaries.
Describe how feature engineering transforms raw data into meaningful inputs for machine learning models.
Differentiate between supervised and unsupervised learning and identify appropriate model types for various problems.
Evaluate machine learning models using key metrics and understand the concepts of overfitting and underfitting.
Grasp the fundamental concepts of deep learning, including neural networks and their architecture.
Understand the basic patterns for deploying and monitoring machine learning models in real-world applications.