Big A Little I - Practical Artificial Intelligence in Python

Overview

Explore practical AI implementation in Python, focusing on real-world applications and techniques for developing intelligent systems.

Syllabus

• Introduction to Artificial Intelligence
Overview of AI and its applications in the real world
Introduction to machine learning and deep learning
Setting up a Python environment for AI development
• Python Basics for AI
Essential Python libraries: NumPy, Pandas, Matplotlib
Data manipulation and visualization techniques
• Introduction to Machine Learning
Supervised learning: regression and classification
Unsupervised learning: clustering and dimensionality reduction
Evaluation metrics and model validation
• Working with Real-World Data
Data preprocessing: cleaning and transformation
Feature engineering and selection techniques
Handling missing data and dealing with outliers
• Building Machine Learning Models in Python
Using Scikit-learn for building and training models
Hyperparameter tuning and model optimization
Cross-validation and model selection techniques
• Introduction to Neural Networks
Basics of neural networks and deep learning
Constructing neural networks with TensorFlow and Keras
Training and evaluating neural networks
• Computer Vision Applications
Image processing techniques
Building image classification models with Convolutional Neural Networks (CNNs)
Use cases: object recognition and image segmentation
• Natural Language Processing (NLP)
Text processing and feature extraction
Sentiment analysis and text classification
Introduction to language models and embeddings
• Reinforcement Learning Basics
Core concepts and terminology
Simple implementations using Python
Practical applications and challenges
• AI in Practice: Case Studies
Review of notable AI projects and applications
Discussing ethical considerations and best practices
Exploring AI trends and future directions
• Capstone Project
Develop a comprehensive AI project using Python
Apply learned techniques to solve a real-world problem
Present findings and reflections on the project
• Course Review and Next Steps
Recap of major topics covered
Guidance on further learning resources and advanced topics
Final Q&A session and feedback collection

Subjects

Conference Talks