AI with Python

Artificial Intelligence Syllabus with Python

Module 1: Introduction to AI and Python Programming

  • What is AI?
    • History and evolution of AI
    • AI vs. Machine Learning vs. Deep Learning
    • Real-world applications of AI
  • Python Programming Basics
    • Python fundamentals: Variables, Data Types, Operators
    • Control structures: Conditional Statements, Loops
    • Functions and Modules in Python
    • Introduction to Python libraries for AI (NumPy, Pandas, Matplotlib)

Module 2: Data Handling and Preprocessing

  • Data Types and Formats
    • Working with structured and unstructured data
    • Reading/Writing CSV, Excel, JSON files using Python
  • Data Cleaning and Preparation
    • Handling missing data, data transformation
    • Feature scaling and normalization
  • Data Visualization
    • Visualizing data using Matplotlib and Seaborn
    • Plotting graphs to understand trends and distributions

Module 3: Machine Learning Fundamentals

  • Introduction to Machine Learning
    • Supervised vs. Unsupervised Learning
    • Training, testing, and validation data
    • Key algorithms: Linear Regression, Decision Trees, k-Nearest Neighbors
  • Working with Scikit-Learn
    • Building machine learning models with Scikit-Learn
    • Model evaluation: Accuracy, Precision, Recall, F1 Score
    • Cross-validation techniques

Module 4: Deep Learning with Neural Networks

  • Introduction to Neural Networks
    • Basics of perceptrons and multi-layered networks
    • Activation functions, forward/backward propagation
  • Using TensorFlow and Keras
    • Building neural networks with Keras
    • Training, tuning, and evaluating neural networks
  • Deep Learning Applications
    • Image classification and processing
    • Natural language processing (NLP) basics

Module 5: Advanced AI Concepts

  • Natural Language Processing (NLP)
    • Tokenization, Stemming, and Lemmatization
    • Sentiment analysis using Python libraries (NLTK, SpaCy)
  • Computer Vision
    • Image recognition and object detection using OpenCV
    • Convolutional Neural Networks (CNNs) for image tasks
  • Reinforcement Learning (Optional/Advanced)
    • Introduction to Reinforcement Learning
    • Building simple reinforcement learning models in Python

Module 6: AI Ethics and Future Trends

  • Ethical AI
    • Bias and fairness in AI
    • AI for social good and responsible AI development
  • Future Trends in AI
    • Current research areas and emerging technologies
    • AI in healthcare, finance, robotics, and autonomous systems

Module 7: Capstone Project

  • Final Project
    • Choose a project that applies AI techniques to a real-world problem
    • Examples: Predicting housing prices, building an image classifier, sentiment analysis for product reviews
    • Present findings and submit a report with code and results

Tools and Libraries

  • Python
    • Python 3.x
  • Libraries
    • NumPy, Pandas, Matplotlib, Seaborn
    • Scikit-Learn
    • TensorFlow, Keras
    • NLTK, SpaCy
    • OpenCV

Module 2: Data Handling and Preprocessing

Objective:

This module focuses on preparing and cleaning data for AI models. Students will learn how to handle various data formats, clean and preprocess data, and visualize it for analysis. These skills are crucial for building robust machine learning models.

Topics Covered:

1. Data Types and Formats

  • Working with Structured and Unstructured Data
    • Introduction to structured (e.g., CSV, Excel) and unstructured data (e.g., text, images).
    • Understanding relational and non-relational data structures.
  • File I/O in Python
    • Reading data from CSV, Excel, and JSON files using pandas.
    • Writing data to different formats for storage and future use.
    Practical Example:
    • Load a dataset (e.g., a CSV file of sales data) into Python using pandas.
    • Explore the dataset: view data types, shape, and summary statistics.

2. Data Cleaning and Preparation

  • Handling Missing Data
    • Identifying missing data in datasets.
    • Techniques for handling missing data: imputation (mean, median, mode) and removing missing values.
  • Data Transformation
    • Converting data types (e.g., categorical to numerical encoding).
    • Date and time formatting: handling timestamps, converting strings to datetime objects.
  • Outlier Detection and Removal
    • Identifying outliers using statistical techniques and visualization.
    • Handling outliers: capping, transformation, or removing them.
    Practical Example:
    • Clean a dataset with missing values and outliers using Python.
    • Perform imputation and detect outliers using box plots and z-scores.

3. Feature Scaling and Normalization

  • Normalization Techniques
    • Min-Max scaling: transforming data to a given range (0,1).
    • Z-score normalization: scaling data to have a mean of 0 and a standard deviation of 1.
  • Importance of Scaling in Machine Learning
    • Why scaling is necessary for distance-based algorithms (e.g., k-Nearest Neighbors, SVM).
    Practical Example:
    • Scale features in a dataset using Scikit-Learn‘s StandardScaler and MinMaxScaler.

4. Data Visualization

  • Visualizing Data Trends
    • Line plots, bar charts, histograms, and scatter plots to visualize distributions and trends.
  • Exploratory Data Analysis (EDA)
    • Using data visualization tools to discover patterns, correlations, and anomalies.
  • Libraries for Visualization
    • Introduction to Matplotlib and Seaborn for advanced visualizations (heatmaps, pair plots).
    Practical Example:
    • Perform EDA on a real-world dataset. Visualize correlations between different features using heatmaps and scatter plots.

Learning Outcomes:

By the end of this module, students will be able to:

  • Import, clean, and manipulate data in Python using pandas.
  • Handle missing data and outliers effectively.
  • Scale and normalize data for machine learning models.
  • Use visualization techniques to explore and present data insights.