Learning Outcomes

Create compelling visual representations of complex datasets using Python libraries.

• Utilize libraries such as Matplotlib, Seaborn, and Plotly
• Design interactive dashboards for data exploration
• Apply best practices in data visualization for effective communication

Gather and process data from various web sources efficiently and ethically.

• Use tools like Beautiful Soup and Scrapy for web scraping
• Handle dynamic content and AJAX calls in web scraping tasks
• Implement ethical scraping practices and respect website policies

Create robust data preprocessing workflows to prepare data for analysis and modeling.

• Perform data cleaning, normalization, and transformation
• Handle missing values and outliers effectively
• Develop scalable preprocessing pipelines using Pandas and NumPy

Utilize popular deep learning frameworks to build and deploy sophisticated AI models.

• Work with TensorFlow and PyTorch for model development
• Implement transfer learning for efficient model training
• Optimize neural network architectures for various tasks

Create and manage APIs to serve machine learning models and data services.

• Develop RESTful APIs using Flask or FastAPI
• Implement proper documentation and testing for APIs
• Ensure security and scalability in API design

Manage the entire lifecycle of machine learning projects from development to deployment.

• Set up CI/CD pipelines for ML projects
• Monitor model performance and implement version control
• Automate model retraining and deployment processes

Create and deploy natural language processing models with effective prompt engineering.

• Work with state-of-the-art NLP models like GPT
• Implement text classification, summarization, and sentiment analysis
• Design effective prompts for various NLP tasks

Develop end-to-end web applications integrating AI and data science components.

• Create backend services using Django or Flask
• Develop frontend interfaces with modern JavaScript frameworks
• Integrate AI models into web applications

Build and fine-tune various machine learning models for optimal performance.

• Implement supervised and unsupervised learning algorithms
• Perform hyperparameter tuning and model selection
• Evaluate and interpret model results effectively

Create and manage databases to support data-intensive applications.

• Work with SQL and NoSQL databases
• Optimize database performance and query execution
• Implement data security and privacy best practices