A comprehensive roadmap to master Machine Learning, from foundational concepts to real-world deployment.
Get started with ML basics, its history, types, and real-world applications.
Covering the essential math: linear algebra, probability, statistics, and calculus.
Mastering the essential Python libraries: NumPy, Pandas, Matplotlib, and Scikit-learn.
Finding, cleaning, and preparing your data for machine learning models.
Learning from labeled data with regression and classification algorithms.
Finding hidden patterns in unlabeled data with clustering and dimensionality reduction.
Measuring how well your model performs and ensuring it generalizes to new data.
An introduction to the building blocks of deep learning.
From building pipelines and deploying models to understanding cloud ML platforms.
Addressing bias, fairness, and the societal impact of machine learning.