Hands-on exercise: Working on the case study using the entire data science workflow
Exploring advanced data visualization libraries: Plotly, Bokeh, or Tableau
Creating interactive and engaging visualizations
Hands-on exercise: Creating interactive visualizations with a chosen library
Techniques for feature importance and selection
Hands-on exercise: Assessing feature importance and selecting relevant features
Introduction to ensemble learning techniques: bagging, boosting, and stacking
Hands-on exercise: Implementing ensemble learning algorithms
Techniques for hyperparameter tuning and model optimization
Hands-on exercise: Optimizing model performance using hyperparameter tuning
Strategies for deploying machine learning models in real-world applications
Model deployment best practices and considerations
Hands-on exercise: Deploying a machine learning model in a production-like environment
Guest lectures by industry professionals or data scientists
Insights into real-world applications of data science in various industries
Q&A session with the industry expert
Clustering algorithms: K-means, hierarchical clustering
Anomaly detection and dimensionality reduction techniques
Hands-on exercise: Applying unsupervised learning techniques to real-world datasets
Introduction to advanced NLP libraries: SpaCy, Gensim, or Transformers
Hands-on exercise: Text preprocessing and advanced NLP tasks
Advanced neural network architectures: CNNs, RNNs, or Transformers
Transfer learning and fine-tuning pre-trained models
Hands-on exercise: Implementing advanced deep learning models
Participants work on improving their capstone project based on feedback
Documenting the project methodology, findings, and conclusions
Creating an impressive portfolio showcasing the final project
