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
At course completion
After completing this course, students will be able to:
Build, and deploy AI solutions using Python, machine learning, deep learning, and generative AI tools. They will be proficient in developing models with frameworks like TensorFlow and Scikit-learn, processing data for AI applications, and integrating models into real-world environments using APIs, Docker, and cloud platforms (AWS, Azure, GCP). Additionally, they will understand ethical AI development, including bias mitigation and explainability, and will complete a full AI project, equipping them with practical experience and a professional portfolio to pursue roles in AI development and data science.
At course completion
After completing this course, students will be able to:
Build, and deploy AI solutions using Python, machine learning, deep learning, and generative AI tools. They will be proficient in developing models with frameworks like TensorFlow and Scikit-learn, processing data for AI applications, and integrating models into real-world environments using APIs, Docker, and cloud platforms (AWS, Azure, GCP). Additionally, they will understand ethical AI development, including bias mitigation and explainability, and will complete a full AI project, equipping them with practical experience and a professional portfolio to pursue roles in AI development and data science.
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: ✔ Understand 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 ✔ Deploy 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
