About AI/ML
Artificial Intelligence and Machine Learning are transforming the way modern technology systems understand data, identify patterns and support intelligent decision making.
The AI/ML course introduces learners to the fundamental concepts behind intelligent systems and machine learning algorithms through a practical learning approach.
The focus is on understanding concepts and applying them through hands-on learning and projects.
Artificial Intelligence
Artificial Intelligence focuses on creating systems capable of performing tasks that require intelligent decision-making, pattern recognition and problem solving.
Machine Learning
Machine Learning enables systems to learn patterns from data and use those patterns to make predictions or support intelligent outcomes.
AI + ML
Together, AI and ML provide the foundation for building technology solutions that can learn from information and become more intelligent in their application.
What you'll Learn
Design and Implement AI & ML Infrastructure:
Develop environments, including data pipelines, model development frameworks, and deployment platforms.
Master AI & ML Algorithms and Techniques:
Apply supervised, unsupervised, reinforcement learning, and deep learning methods to solve challenges.
Develop Intelligent Troubleshooting Agents:
Create AI-powered agents capable of diagnosing and resolving issues autonomously.
Leverage Microsoft Azure for AI & ML Workflows:
Set up, manage, and optimize the entire AI & ML lifecycle using Azure.
Course Curriculam
Build a comprehensive foundation in Artificial Intelligence and Machine Learning by understanding the core concepts, learning approaches, algorithms, and practical applications used to develop intelligent systems.
You’ll explore:
- Introduction to Artificial Intelligence and Machine Learning
- Understanding AI, ML, and deep learning concepts
- Supervised, unsupervised, and reinforcement learning
- Data preparation, feature engineering, and model training
- Machine learning algorithms and model evaluation
- Neural networks and deep learning fundamentals
- Practical AI and machine learning workflows using Python
- Real-world applications and use cases of AI and ML
Explore commonly used AI and Machine Learning algorithms and learn how to select, train, evaluate, and optimize models for different types of real-world problems.
You’ll explore:
- Regression and classification algorithms
- Decision trees, random forests, and ensemble methods
- Clustering and dimensionality reduction
- Feature selection and feature engineering
- Model training, validation, and testing
- Performance metrics and model evaluation
- Hyperparameter tuning and model optimization
- Choosing appropriate algorithms for business problems
Learn how AI and Machine Learning can be applied to build intelligent troubleshooting solutions that analyze problems, identify patterns, recommend actions, and assist users in resolving technical issues.
You’ll explore:
- Introduction to intelligent troubleshooting systems
- Understanding AI-powered diagnostic workflows
- Collecting and analyzing troubleshooting data
- Pattern recognition and anomaly detection
- Building intelligent recommendation workflows
- Natural language interaction for troubleshooting
- Integrating AI models with support and service workflows
- Evaluating and improving intelligent troubleshooting solutions
Learn how Microsoft Azure provides cloud-based services and tools for developing, training, deploying, and managing AI and Machine Learning solutions at scale.
You’ll explore:
- Introduction to Azure AI and Machine Learning services
- Azure Machine Learning workspace and environment
- Preparing and managing datasets in Azure
- Training and evaluating machine learning models
- Deploying machine learning models as services
- Azure AI services and intelligent application capabilities
- Monitoring and managing deployed AI solutions
- Security, scalability, and responsible AI considerations
Explore advanced AI and Machine Learning techniques used to build more capable intelligent systems. Apply modern approaches to complex data, predictive modeling, automation, and AI-powered applications.
You’ll explore:
- Advanced machine learning model optimization
- Deep learning and neural network architectures
- Natural Language Processing fundamentals
- Computer vision and image-based AI applications
- Generative AI and Large Language Model fundamentals
- Advanced feature engineering and model evaluation
- AI automation and intelligent application development
- Model deployment, monitoring, and continuous improvement
- Practical AI and Machine Learning project development
