1. Introduction to AI and Python
- AI Basics: Understand what AI is, including key concepts like machine learning, deep learning, and neural networks.
- Python Basics: Learn Python fundamentals, including syntax, data types, functions, and libraries.
2. Setting Up Your Environment
- Python Installation: Install Python from python.org.
- IDE: Choose an Integrated Development Environment (IDE) like Jupyter Notebook, PyCharm, or VS Code.
3. Data Collection and Preprocessing
- Data Collection: Gather data from various sources like APIs, CSV files, databases, or web scraping.
- Data Cleaning: Handle missing values, remove duplicates, and correct inconsistencies.
- Data Transformation: Normalize or standardize data, encode categorical variables, and split data into training and testing sets.
4. Exploratory Data Analysis (EDA)
- Visualization: Use libraries like Matplotlib and Seaborn to visualize data distributions and relationships.
- Statistics: Calculate basic statistics to understand the data better.
5. Building and Training Models
- Choosing a Model: Decide on the type of model based on the problem (e.g., regression, classification, clustering).
- Model Implementation: Use libraries like Scikit-learn, TensorFlow, or Keras to build your model.
