Machine learning Azure Tutorial

An introduction to some of the most popular Azure Machine Learning service functionalities is provided in this tutorial. You will build, register, and use a model in it. You will learn about the fundamental ideas behind Azure Machine Learning and how they are most frequently used in this tutorial.
You will gain knowledge on how to launch a training job onto a scalable computational resource, test the deployment, and end the job.

The following step you'll take are:

Set up a handle to your Azure Machine Learning workspace
Create your training script
Create a scalable compute resource, a compute cluster
Create and run a command job that will run the training script on the compute cluster, configured with the appropriate job environment
View the output of your training script
Deploy the newly-trained model as an endpoint
Call the Azure Machine Learning endpoint for inferencing

Required before start

To use Azure Machine Learning, you'll first need a workspace.
Sign in to studio and select your workspace if it's not already open.
Open or create a notebook in your workspace:

For full article take refrence of below link:-

machine-learning

Table of content

Introduction to Machine Learning
Types of Machine Learning
- Types of Classification in Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Data Preprocessing
- Feature Engineering for Machine Learning
- Handling Missing Data
- Data Normalization and Standardization
- Outlier Detection for Machine Learning
Machine Learning Models
- Linear Regression
- Logistic Regression
- Decision Trees
- Understanding Decision Trees for Regression
- Support Vector Machines (SVM)
- Random Forests
- Neural Networks
Model Deployment
- Deploy Salary Prediction Model on Heroku
- Deploying ML Models with Flask
- Using Docker for Model Deployment
Advanced Machine Learning Concepts
- Hyperparameter Tuning
- Cross-Validation Techniques
- Ensemble Learning (Bagging and Boosting)
- Dimensionality Reduction Techniques (PCA, LDA)
Deep Learning Basics
- Introduction to Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Transfer Learning
Real-World Applications
- Natural Language Processing (NLP)
- Image Recognition
- Recommendation Systems
- Predictive Analytics
Machine Learning Tools and Libraries
- Python and scikit-learn
- TensorFlow and Keras
- PyTorch
- Apache Spark MLlib
Interview Preparation
- Basic Machine Learning Interview Questions
- Scenario-Based Questions
- Advanced Machine Learning Concepts
Best Practices in Machine Learning
- Performance Optimization
- Handling Imbalanced Datasets
- Model Explainability (SHAP, LIME)
- Security and Bias Mitigation
FAQs and Troubleshooting
- Frequently Asked Questions
- Troubleshooting Common ML Errors
Resources and References
- Recommended Books
- Official Documentation
- Online Courses and Tutorials