Introduction
ML transforms raw data into actionable insights, powering business analytics, forecasting, and process improvement. ML training is essential for preparing teams to lead in data science and modern analytics.
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Core Modules Covered
ML Concepts
Supervised/unsupervised learning, regression, clustering.
Data Preprocessing
Cleaning, feature engineering, normalization.
Model Building
Selection, training, validation, accuracy and reliability.
Frameworks
Python (Scikit-learn, TensorFlow), R, Spark MLlib.
Deployment
Model integration, monitoring, maintenance.
Use Cases
Fraud detection, customer segmentation, operational analytics.
Project Management
Agile ML lifecycle, team collaboration.
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Learning Methods
Instructor-led Coding Labs
Instructor-led coding labs, use case workshops.
Real Company Datasets
Real company datasets for experiential learning.
Assessment & Competitions
Assessment via project deliverables and competitions.
Business Outcomes
- More accurate, reliable business decision-making.
- Rapid deployment and scaling of analytical solutions.
- Culture of continuous learning and innovation.
Key Takeaways
- Robust data science and ML skills embedded in teams.
- Confidence to deliver high-value insights from business data.
- Foundational prep for advanced certifications.