Week-1: Building the Foundations of Artificial Intelligence
- Overview of Artificial Intelligence (AI): History, concepts, and applications in real-world scenarios.
- Introduction to AI terminologies: Machine learning, deep learning, neural networks, and data science.
- Setting up the environment: Installing Python and essential libraries (NumPy, Pandas, TensorFlow, etc.).
- Basics of Python programming for AI: Data structures, loops, and functions.
- Assignment: Write a Python script to perform basic data operations (e.g., calculating the average or sorting data).
Week-2: Understanding Machine Learning Fundamentals
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- Introduction to machine learning: Supervised, unsupervised, and reinforcement learning.
- Data preprocessing: Cleaning, transforming, and visualizing datasets.
- Building the first machine learning model using Scikit-learn.
- Introduction to evaluation metrics: Accuracy, precision, recall, and F1-score.
- Assignment: Train a simple machine learning model (e.g., linear regression) on a given dataset and analyze its performance.
