Beginner AWS Machine Learning Learning Path

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AWS Machine Learning and AI Complete Course

Comprehensive deep learning specialization by Andrew Ng. Master neural networks, CNNs, RNNs, and modern deep learning architectures.

This course covers the entire process of building, evaluating, and operationalizing machine learning models. You will learn to assess model performance using key metrics and cross-validation techniques and explore methods for improving model efficiency.

This course focuses on the theory and practice of various classification algorithms in machine learning.

This course introduces the importance of quality data in machine learning. It covers techniques to retrieve, clean, and apply feature engineering to data, preparing it for preliminary analysis and hypothesis testing.

This course provides a comprehensive overview of how machine learning functions in embedded systems. It teaches students how to train neural networks and deploy them to microcontrollers, a field also known as TinyML. The course is designed for beginners with no prior machine learning experience, but some familiarity with Arduino and microcontrollers is recommended.

Explore supervised machine learning algorithms, prediction tasks, and model selection. Learn to improve performance using linear/logistic regression, KNN, decision trees, ensembling methods, and kernel techniques like SVM.

This course includes a dedicated module on Data QA & Profiling. It covers techniques for univariate and multivariate profiling, common data quality issues like missing values, and data visualization for profiling.

Learn about the framework of online learning, where algorithms make sequential decisions and learn from feedback.

This course focuses on the practical application of machine learning techniques in Python. It covers a variety of supervised and unsupervised learning methods and their implementation using the Scikit-Learn library.

This course focuses on uncovering hidden structures from unlabeled data. It covers Principal Component Analysis (PCA) for dimension reduction and popular clustering methods like K-means and hierarchical clustering.

A foundational lecture on the gradient descent algorithm within the context of the highly-rated Machine Learning course.

A professional certificate program that covers essential machine learning algorithms, including decision trees and ensemble methods.