Unlock the true power of Artificial Intelligence and Machine Learning by starting with the right data foundation. This presentation guides you through essential practices for preparing data—covering data collection, cleaning, transformation, and validation. Learn how structured, high-quality data enables better model accuracy and performance. We also explore real-world use cases and tips to avoid common pitfalls in AI/ML data workflows. Perfect for analysts, data scientists, and decision-makers aiming to maximize project outcomes through data readiness.

1. PreparingYour
DataforAI&ML
Success
Presented by Statswork

2. Understanding the
Importance of Data
Preparation
High-Quality Data is Essential
It enhances insights and decision-making processes.
Reducing Noise and Bias
Improves accuracy and reliability of AI models.
Compliance and Governance Standards
Ensures adherence to regulations and best practices.
Building Trust in Data
Fosters confidence among stakeholders and users.

3. Transformation & Feature
Engineering
Normalize and create new
features for analysis.
Data Collection
Gather structured and
unstructured data effectively.
Cleaning & Preprocessing
Remove duplicates and correct
errors systematically.

4. Dimensionality Reduction
Reduces complexity while
preserving important
information.
Data Normalization
Essential for consistent model
performance and accuracy.
Creating New Features
Enhances model capability and
insight extraction.

5. Data Governance
Establishing policies enhances
trust and compliance in AI
initiatives.
Splitting and Monitoring
Enforcing governance and
maintaining data quality is crucial.
Quality Assurance
Implementing checks ensures
reliability and accuracy of your
dataset.

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