Foundations of Machine Learning with Azure
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Foundations of Machine Learning with Azure
This course is part of Azure ML Bootcamp: Machine Learning on the Cloud Specialization
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What you'll learn
Understand the core concepts and types of machine learning (supervised, unsupervised, reinforcement learning).
Explore the Azure ecosystem and learn how Azure Machine Learning supports ML workflows.
Learn how to source, clean, and preprocess data for machine learning tasks.
Gain hands-on experience with data transformation, normalization, encoding, and handling missing data in Azure ML.
Skills you'll gain
- Model Training
- Machine Learning Software
- Machine Learning
- Applied Machine Learning
- Cloud Deployment
- Feature Engineering
- Machine Learning Algorithms
- Data Preprocessing
- Data Transformation
- Supervised Learning
- Machine Learning Methods
- Cloud Services
- Data Processing
- Artificial Intelligence and Machine Learning (AI/ML)
- Data Quality
- Data Cleansing
Tools you'll learn
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There are 2 modules in this course
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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will gain a foundational understanding of machine learning (ML) and how it is implemented on Microsoft Azure's cloud platform. You will begin by learning the fundamental concepts of machine learning, including types of learning, such as supervised, unsupervised, and reinforcement learning. With real-world case studies, you will explore how these ML techniques are applied in industries like healthcare, finance, and retail. You will also be introduced to the most important challenges in machine learning, such as overfitting, underfitting, and data quality concerns. As the course progresses, you'll dive into Azure Machine Learning Studio, understanding its interface, capabilities, and key features such as AutoML, data integration, and model management. You will learn how to set up experiments, connect to data sources, manage resources, and deploy machine learning models efficiently. The course will include practical demos to help solidify your understanding of data preprocessing, from importing and cleaning datasets to splitting and normalizing them for model training. By leveraging Azure’s flexible tools, you'll become comfortable with handling data, building, and deploying machine learning models. This course is designed for beginners and intermediate learners eager to gain hands-on experience with machine learning using Azure. It’s ideal for individuals looking to deepen their ML knowledge, as well as professionals looking to integrate machine learning into business solutions. The prerequisites include a basic understanding of programming and data science concepts, and an eagerness to explore machine learning through a cloud computing platform. By the end of the course, you will be able to build machine learning models, preprocess and clean datasets, utilize Azure’s tools for model training and deployment, and solve common ML challenges such as data imbalances and overfitting.
In this module, we will introduce you to the basics of machine learning, covering key concepts like types of learning, data, models, and predictions. You’ll explore the essential features of Azure Machine Learning Studio and how it supports the development of machine learning models. By the end of this section, you will be familiar with both machine learning fundamentals and how Azure facilitates the end-to-end ML process.
What's included
20 videos2 readings1 assignment
20 videos•Total 159 minutes
- Definition and Overview of Machine Learning (ML)•5 minutes
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning•7 minutes
- Key Concepts: Training Data, Features, Labels, Models, Predictions•7 minutes
- Real-World Applications of ML in Industries such as Healthcare, Finance, and Retail•9 minutes
- Challenges in Machine Learning: Overfitting, Underfitting, Data Quality, and Interpretability•7 minutes
- Introduction to Azure ML Studio and Its Capabilities for Building, Training, and Deploying Models•7 minutes
- Overview of the Azure Machine Learning Workspace: Datasets, Experiments, Models•6 minutes
- Key Components: Designer, Notebooks, Automated ML, and Model Management•6 minutes
- Key Features: Visual Interface, AutoML, Integration with Azure Services (Data Factory, Blob Storage, etc.)•5 minutes
- Scalability and Flexibility with Azure Compute and Storage Options•5 minutes
- Collaboration and Sharing: Team-Based Development and Version Control•5 minutes
- Benefits: Faster Experimentation, Model Deployment, and Continuous Learning•5 minutes
- Creating an Azure Account•4 minutes
- Exploring Azure Cloud Interface and Services Part 1•11 minutes
- Exploring Azure Cloud Interface and Services Part 2•13 minutes
- Exploring Azure Cloud Interface and Services Part 3•11 minutes
- Creating Azure ML Studio•11 minutes
- Exploring Key Features and Benefits of Azure ML Studio•16 minutes
- Overview of Resource Management: Workspaces, Compute Resources, and Storage Accounts•11 minutes
- Connecting to Data Sources and Azure Services•10 minutes
2 readings•Total 20 minutes
- Introduction to the Course 'Foundations of Machine Learning with Azure'•10 minutes
- Full Specialization Resources•10 minutes
1 assignment•Total 15 minutes
- Introduction to Machine Learning and Azure - Assessment•15 minutes
In this module, we will focus on the essential steps in preparing data for machine learning. You will learn how to clean datasets, handle missing values, and apply normalization and scaling techniques. Additionally, we will dive into advanced concepts like feature selection and transformation, ensuring your data is ready for model training in Azure ML Studio.
What's included
19 videos1 reading3 assignments
19 videos•Total 155 minutes
- Importing Datasets from Various Sources: Local Files, Azure Blob Storage, SQL Databases, etc.•7 minutes
- Exploring Dataset Statistics and Visualizing Data Distribution•7 minutes
- Understanding Data Types: Numerical, Categorical, Text, Image•6 minutes
- Identifying and Handling Missing Data (Null, NaN Values)•6 minutes
- Outlier Detection and Treatment Strategies•6 minutes
- Removing Duplicates and Irrelevant Issues•4 minutes
- Correcting Data Types and Formatting Issues•4 minutes
- DEMO - Cleaning a Dataset by Handling Missing Values and Outliers in ML Studio•17 minutes
- Splitting Datasets into Training, Validation, and Test Sets•6 minutes
- Random Sampling and Stratified Sampling Techniques•5 minutes
- Data Normalization and Scaling Techniques: MinMax Scaling, Standardization (Z-score)•6 minutes
- Handling Imbalanced Datasets and Using Oversampling & Undersampling Techniques•6 minutes
- DEMO - Splitting and Normalizing a Dataset in Azure ML Studio•18 minutes
- Creating New Features Through Transformations (Logarithmic, Polynomial Features)•3 minutes
- Introduction to Feature Selection: Choosing Relevant Features for Model Training•6 minutes
- Encoding Categorical Variables (One-Hot Encoding, Label Encoding)•6 minutes
- Feature Selection and Transformation•6 minutes
- Data Transformation & Augmentation•9 minutes
- Exploring ML Studio Designer and Setting Up an Experiment•29 minutes
1 reading•Total 10 minutes
- Conclusion to the Course 'Foundations of Machine Learning with Azure'•10 minutes
3 assignments•Total 90 minutes
- Full Course Practice Assessment•15 minutes
- Data Basics and Preprocessing - Assessment•15 minutes
- Full Course Assessment•60 minutes
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Frequently asked questions
Machine Learning (ML) is a branch of artificial intelligence that focuses on creating algorithms that allow computers to learn from and make decisions based on data. It is increasingly relevant across industries like healthcare, finance, and retail, where it can automate tasks, uncover insights, and make data-driven predictions, improving efficiency and decision-making processes.
The "Foundations of Machine Learning with Azure" course introduces learners to the basics of machine learning and how it integrates with Microsoft Azure's cloud platform. It covers key concepts of machine learning, types of algorithms, and real-world applications. The course also provides hands-on experience with Azure ML Studio, guiding learners through data preprocessing, model building, and deployment using Azure's powerful tools and resources.
After completing this course, you will be able to understand the fundamentals of machine learning and apply these concepts using Azure ML Studio. You’ll be able to preprocess data, build and train machine learning models, and deploy them using Azure's cloud services. Additionally, you will have the skills to work with datasets, implement data transformation techniques, and handle real-world machine learning challenges such as overfitting and data imbalances.
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