Day 1:
Introduction to Python for AI
Overview of Python programming and its relevance in AI.
Setting up the Python environment and essential tools.
Introduction to Python libraries: NumPy, pandas, and Matplotlib.
Writing Python scripts and understanding key programming concepts.
Day 2:
Fundamentals of Artificial Intelligence and Machine Learning
Understanding the basics of AI, machine learning, and deep learning.
Exploring supervised, unsupervised, and reinforcement learning techniques.
Introduction to data preprocessing and feature engineering.
Implementing basic machine learning models using scikit-learn.
Day 3:
Advanced AI Techniques with Python
Building deep learning models with TensorFlow and Keras.
Training and evaluating neural networks for classification and regression tasks.
Exploring natural language processing (NLP) and computer vision techniques.
Introduction to transfer learning and pre-trained models.
Day 4:
AI Model Deployment and Best Practices
Preparing AI models for deployment in production environments.
Using Flask and Django for deploying AI applications.
Monitoring and maintaining AI systems post-deployment.
Addressing challenges in scalability, latency, and real-time AI solutions.
Day 5:
Ethics, Real-World Applications, and Capstone Project
Exploring ethical considerations in AI programming and development.
Case studies of AI applications in various industries.
Hands-on capstone project: Building an end-to-end AI solution.
Final review, feedback, and Q&A session with the instructor.
