Intermediate Azure Machine Learning Learning Path

But what is a neural network? | Deep learning chapter 1

The Essential Main Ideas of Neural Networks

Transformers Explained - How transformers work

Learn Machine Learning Like a GENIUS and Not Waste Time

All Machine Learning algorithms explained in 17 min

Learn What is YOLO algorithm? | Deep Learning Tutorial 31 (TensorFlow, Keras & Python)

Neural Networks and Deep Learning: Crash Course AI 3

Natural Language Processing: Crash Course AI 7

A visual introduction to neural networks and deep learning. This series explains the fundamentals of neural networks with beautiful animations and intuitive explanations.

Master the fundamentals of machine learning with this comprehensive course from Stanford University

A comprehensive course on Udemy that covers building, training, and deploying machine learning models using Microsoft Azure ML Studio, including no-code and Python-based approaches. It covers AutoML as a key component of the Azure ML platform.

This course provides a thorough introduction to the caret package in R for building and evaluating supervised learning models.

This course covers the fundamentals of tree-based models, including decision trees, random forests, and gradient boosting. You will learn how to build, tune, and evaluate these models using Python's Scikit-Learn library.

This course teaches you how to use tree-based models and ensembles for classification and regression in R.

This course provides a deep dive into regression analysis using Python. You will learn about simple and multiple linear regression, as well as techniques for model evaluation and selection.

This course focuses on regularization techniques, such as Ridge and Lasso regression, which are used to prevent overfitting in machine learning models. You will learn the theory behind these techniques and how to apply them in practice.

Learn to generate, explore, and evaluate machine learning models in R using the Tidyverse. The course covers multiple and logistic regression, tree-based models, and support vector machines.

This course covers four of the most common classification algorithms in R: k-nearest neighbors, logistic regression, Naive Bayes, and decision trees.

This track covers the fundamental concepts of machine learning, with a focus on supervised learning techniques using Python.