About This Course
This course provides an in-depth and practical foundation for becoming an AI Developer. It is designed for technical professionals seeking to build, train, and deploy AI models using real-world tools and frameworks. Participants will explore machine learning, deep learning, and generative AI while developing and deploying intelligent applications using Python, TensorFlow/Keras, and cloud services. The course also emphasizes ethical AI development and prepares learners for real-world integration and deployment.
Audience Profile
This course is intended for:
• Developers and software engineers
• Data scientists and analysts
• Technical professionals interested in AI development
• Individuals aiming to build and deploy AI models in production environments
Course Outline
Module 1: Introduction to AI Development
- AI vs ML vs Deep Learning: Key distinctions
- Overview of AI development lifecycle
- Setting up the AI development environment (Python, Jupyter, Colab) Learning Outcomes:
- Understand AI development stages
- Set up tools for AI development
- Recognize key AI domains and use cases
Module 2: Python for AI Developers
- Essential Python libraries: Numpy, Pandas, Matplotlib
- Data preprocessing and feature engineering
- Exploratory Data Analysis (EDA) for AI models Learning Outcomes:
- Process and analyze data
- Prepare data for model input
- Visualize and interpret datasets
Module 3: Machine Learning Algorithms
- Supervised and Unsupervised Learning (Regression, Classification, Clustering)
- Building ML models with Scikit-learn
- Model evaluation and optimization techniques Learning Outcomes:
- Build ML models from scratch
- Evaluate and optimize ML models
- Select the right algorithm for the problem
Module 4: Deep Learning with Neural Networks
- Neural Networks: Architecture and training process
- Using TensorFlow and Keras to build models
- Applications: Image classification and basic NLP Learning Outcomes:
- Build deep learning models
- Train and evaluate neural networks
- Apply DL models to real-world tasks
Module 5: Natural Language Processing (NLP)
- Text preprocessing, tokenization, and embeddings
- Sentiment analysis and text classification
- Introduction to Transformers (BERT, GPT) Learning Outcomes:
- Process and analyze text data
- Build NLP models
- Utilize pre-trained language models
Module 6: Generative AI and Large Language Models (LLMs)
- Overview of Generative AI and LLMs
- Using OpenAI API and other LLM platforms
- Fine-tuning models for specific tasks Learning Outcomes:
- Understanding LLM capabilities
- Use LLMs for content generation
- Customize AI model outputs
Module 7: Model Deployment and Integration
- Deploying AI models using Flask, FastAPI, and Streamlit
- Creating APIs for AI model access
- Dockerizing AI applications for deployment Learning Outcomes:
- Deploy models as web services
- Create scalable AI APIs
- Package and deploy using Docker
Module 8: AI in the Cloud (AWS, Azure, GCP)
- Overview of AI services in the cloud
- Training and deploying models in cloud environments
- Serverless AI workflows and automation Learning Outcomes:
- Utilize cloud AI tools
- Deploying AI models at scale
- Automate workflows using cloud platforms
Module 9: AI Ethics, Bias, and Responsible Development
- Understanding AI fairness and bias
- Ensuring transparency and explainability
- Ethical deployment of AI models Learning Outcomes:
- Develop responsible AI applications
- Identify and mitigate AI bias
- Implement explainable AI practices
Module 10: Final Project: Full AI Application Development
- Define and scope a real-world AI use case
- Build, train, and deploy an AI solution
- Present and document the end-to-end AI workflow Learning Outcomes:
- Execute a complete AI project
- Integrate AI into an application
- Present AI-driven solutions professionally
Prerequisites
Participants should have:
- Basic programming knowledge (preferably in Python)
- Familiarity with software development concepts
- Interest in AI, data, and building intelligent applications
