Python Language – Use in AI Course

Aim

Program Objectives

Program Structure

Module 1: Introduction to Python for AI

• Overview of Python and its relevance to AI.
• Setting up Python for AI development: Anaconda, Jupyter Notebooks, and IDEs.
• Introduction to essential Python libraries for AI: Numpy, Pandas, and Matplotlib.
• Hands-on exercise: Writing basic Python scripts for AI tasks.

Module 2: Data Preprocessing and Visualization

• Data cleaning and preprocessing using Python (handling missing data, feature scaling, encoding categorical data).
• Data visualization with Matplotlib and Seaborn for AI model insights.
• Understanding data types and structures for AI applications.
• Hands-on exercise: Preprocessing a real-world dataset and visualizing it for insights.

Module 3: Machine Learning with Python

• Introduction to supervised learning: regression and classification algorithms.
• Unsupervised learning techniques: clustering and dimensionality reduction.
• Using Scikit-learn for building machine learning models: linear regression, decision trees, random forests, and k-NN.
• Hands-on exercise: Implementing and training machine learning models on sample data.

Module 4: Introduction to Deep Learning with Python

• Understanding neural networks and deep learning fundamentals.
• Using Keras and TensorFlow for building deep learning models.
• Building and training basic neural networks: multi-layer perceptrons (MLP).
• Hands-on exercise: Building a deep learning model for image classification or text analysis.

Module 5: Advanced Deep Learning Techniques

• Exploring convolutional neural networks (CNNs) and their applications in image processing.
• Recurrent neural networks (RNNs) for time series prediction and sequence data analysis.
• Implementing advanced deep learning models with Keras and TensorFlow.
• Hands-on exercise: Implementing CNN and RNN models for real-world problems.

Module 6: Model Evaluation and Optimization

• Evaluating AI models: accuracy, precision, recall, F1-score, and confusion matrix.
• Hyperparameter tuning and optimization techniques for improving model performance.
• Cross-validation and model selection techniques for better generalization.
• Hands-on exercise: Tuning machine learning and deep learning models for optimal performance.

Module 7: AI Model Deployment with Python

• Deploying machine learning and deep learning models using Python frameworks (Flask, FastAPI).
• Building APIs for model inference and integration into applications.
• Deploying models in production environments: cloud services, Docker, and Kubernetes.
• Hands-on exercise: Deploying an AI model as a web service for real-time predictions.

Module 8: AI Ethics and Responsible AI

• Understanding the ethical implications of AI development and deployment.
• Ensuring fairness, transparency, and accountability in AI models.
• Addressing bias and privacy concerns in AI systems.
• Hands-on exercise: Evaluating and mitigating bias in AI models.

Final Project

• Develop an end-to-end AI solution using Python, from data preprocessing to model deployment.
• Apply machine learning or deep learning techniques to a real-world problem.
• Example projects: Predicting house prices, building a sentiment analysis system, or creating a recommendation engine.

Who Should Enrol?

Program Outcomes

Fee Structure

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