Build on your existing knowledge with intermediate azure machine learning techniques and real-world applications.
Not typically required
Confident developer; infrastructure scripting
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IntermediateMachine Learning by Stanford
IntermediateThe complete Azure Machine learning course - 2025 Edition
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BeginnerMachine Learning with Tree-Based Models in Python
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AdvancedRegularization for Machine Learning
IntermediateSupervised Machine Learning in R
IntermediateSupervised Machine Learning in R: Classification
IntermediateSupervised Machine Learning in Python
IntermediateBut what is a neural network? | Deep learning chapter 1
IntermediateThe Essential Main Ideas of Neural Networks
IntermediateTransformers Explained - How transformers work
IntermediateLearn Machine Learning Like a GENIUS and Not Waste Time
IntermediateAll Machine Learning algorithms explained in 17 min
IntermediateWhat is YOLO algorithm? | Deep Learning Tutorial 31 (Tensorflow, Keras & Python)
IntermediateNeural Networks and Deep Learning: Crash Course AI #3
IntermediateNatural Language Processing: Crash Course AI #7
IntermediateNeural Networks - 3Blue1Brown
IntermediateMachine Learning by Stanford
IntermediateThe complete Azure Machine learning course - 2025 Edition
IntermediateMachine Learning with caret in R
BeginnerMachine Learning with Tree-Based Models in Python
IntermediateMachine Learning with Tree-Based Models in R
IntermediateRegression Analysis in Python
AdvancedRegularization for Machine Learning
IntermediateSupervised Machine Learning in R
IntermediateSupervised Machine Learning in R: Classification
IntermediateSupervised Machine Learning in Python
IntermediateFollow these courses in order to complete the learning path. Click on any course to enroll.
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.
Explore related content to expand your skills beyond this learning path.
Enroll in this path to track your progress and stay motivated.