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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

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