Real-World Dataset Handling
Learn to handle and preprocess real-world datasets for AI and ML applications.
Learning Objectives
Develop skills to prepare datasets for model training and evaluation.
Topics
Data Cleaning Techniques
Explore techniques for cleaning datasets.
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Feature Engineering
Understand the process of feature engineering.
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Data Transformation Methods
Learn methods to transform data for better model performance.
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Dealing with Missing Data
Identify strategies to handle missing data in datasets.
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Data Augmentation
Explore the concept and techniques of data augmentation.
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Balancing Imbalanced Datasets
Learn strategies to handle imbalanced datasets.
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Big Data Management
Understand the challenges and techniques in managing big data.
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Case Study: Dataset Preparation
Analyze a case study on preparing datasets for AI models.
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Realtime audio conversation for interactive session.
Interactive realtime chat session.
Live whiteboard explanation and collaboration.
Real-time wide variety of examples.
Continuous assessment and feedback.
Progress monitoring and record progress journey.
Broadcast session with larger audience for free.
Attend audience queries and provide responses.