Data Preparation and Feature Processing
High-quality data remains the backbone of successful machine learning applications. It explores techniques for handling missing values through missing data imputation and reducing bias caused by inconsistencies. It also emphasizes transforming raw data into meaningful representations using feature scaling, normalization, and one-hot encoding. Learners study how preprocessing choices influence model accuracy and generalization. Case studies demonstrate improved outcomes when proper data pipelines are implemented. It strengthens practical readiness for model development.
Data Engineering Focus:
- Data cleaning and transformation
- Feature encoding and scaling
- Dataset structuring for ML pipelines
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Data Preparation and Feature Processing 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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