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Course Outline
1. Introduction to AI Engineering
- Defining AI Engineering
- Differences Between AI, Machine Learning, and Deep Learning
- The AI Engineering Lifecycle
- AI Applications Across Industries
- Roles and Responsibilities of an AI Engineer
2. Foundations of Artificial Intelligence
- Core AI Concepts and Terminology
- Supervised, Unsupervised, and Reinforcement Learning
- Fundamentals of Neural Networks and Deep Learning
- Overview of Generative AI and Foundation Models
- Ecosystems and Frameworks for AI Development
3. Python for AI Engineering
- Essential Python Libraries for AI
- NumPy, Pandas, and Matplotlib
- Data Manipulation and Visualization Techniques
- Working with Jupyter Notebooks
- Writing Reusable Code for AI
4. Data Preparation for AI
- Collecting and Analyzing Datasets
- Data Cleaning and Preprocessing
- Feature Engineering Techniques
- Feature Scaling and Normalization
- Splitting Data into Training, Validation, and Test Sets
- Handling Missing Values and Outliers
5. Machine Learning Fundamentals
- Regression Algorithms
- Classification Algorithms
- Clustering Techniques
- The Model Training Workflow
- Evaluating Model Performance Metrics
- Avoiding Overfitting and Underfitting
6. Building AI Models with TensorFlow and PyTorch
- Introduction to TensorFlow
- Introduction to PyTorch
- Designing Neural Networks
- Model Training and Validation Processes
- Saving and Loading Models
- Comparing Frameworks: TensorFlow vs. PyTorch
7. Natural Language Processing (NLP) Fundamentals
- Text Preprocessing Techniques
- Understanding Word Embeddings
- Text Classification Methods
- Sentiment Analysis
- Introduction to Transformer Models
- Practical NLP Applications
8. AI in Software Development
- Integrating AI into Existing Applications
- Accessing AI Services via APIs
- Developing AI-Powered Applications
- AI-Assisted Software Development Tools
- Testing AI-Enabled Applications
9. Best Practices in AI Engineering
- Effective Project Organization
- Version Control with Git
- Experiment Tracking Strategies
- Model Versioning Techniques
- Documentation Standards
- Ensuring Reproducibility in AI Projects
10. Deploying AI Models
- Model Serialization Methods
- Building Inference Services
- Creating REST APIs for AI Models
- Introduction to Docker for AI Deployment
- Monitoring Deployed Models
- Maintenance and Updates for Models
11. Data Engineering for AI
- Building Data Pipelines
- ETL (Extract, Transform, Load) Processes
- Managing Structured and Unstructured Data
- Data Storage Solutions
- Data Quality Management
- Preparing Production-Ready Datasets
12. Responsible and Ethical AI
- Addressing AI Bias and Ensuring Fairness
- Explainable AI (XAI)
- Privacy and Data Protection
- Security Considerations in AI
- Principles of Responsible AI Development
- Regulatory and Governance Frameworks
13. Managing AI Projects
- The AI Project Lifecycle
- Applying Agile Methodologies to AI Projects
- Fostering Collaboration Between Technical and Business Teams
- Estimating AI Project Requirements
- Risk Management Strategies
- Evaluating Project Success
14. Practical AI Engineering Workshop and Emerging Trends
- Setting Up a Complete AI Development Workflow
- Constructing an End-to-End Machine Learning Project
- Training and Evaluating Models with TensorFlow or PyTorch
- Deploying a Simple AI Application
- Current Trends in AI Engineering
- Generative AI and Large Language Models (LLMs)
- MLOps and AI Automation
- Career Paths and Continuous Learning Opportunities
- Summary, Q&A Session, and Next Steps
Requirements
- Familiarity with fundamental programming concepts
- Hands-on experience with Python programming
- Basic understanding of statistics and linear algebra
Target Audience
- AI engineers
- Software developers
- Data analysts
14 Hours
Testimonials (2)
The extensive selection of tools presented
Miruna Buzduga - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Step by step training with a lot of exercises. It was like a workshop and I am very glad about that.