Exploratory Data Analysis and Visualization
Learners engage in understanding datasets through statistical summaries and graphical methods. Visual exploration helps uncover patterns, correlations, and anomalies using tools based on probability distributions and correlation coefficients. Students learn to interpret relationships that support statistical inference and model assumptions. Techniques such as histograms, scatter plots, and heat maps assist in identifying trends. This encourages critical thinking and hypothesis generation before model selection. Strong EDA practices improve both insight and performance.
Analytical Techniques:
- Statistical profiling and summaries
- Visual pattern identification
- Insight-based feature refinement
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Exploratory Data Analysis and Visualization Conference Speakers
Recommended Sessions
- Capstone Project
- Classification Algorithms and Decision Systems
- Clustering and Unsupervised Pattern Discovery
- Convolutional Neural Networks (CNNs)
- Data Preparation and Feature Processing
- Deployment of ML Models
- Dimensionality Reduction and Feature Selection
- Ensemble Learning Approaches
- Ethics and Responsible AI
- Exploratory Data Analysis and Visualization
- Feature Engineering and Representation Design
- Foundations of Machine Learning
- Generative Models and Creativity in AI
- Introduction to Deep Learning
- Model Evaluation and Performance Validation
- Natural Language Processing Basics
- Neural Network Architecture and Optimization
- Optimization and Hyperparameter Tuning
- Recurrent Neural Networks (RNNs)
- Regression-Based Learning Models
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