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Learning Journey
Kursus Program dan Silabus
DATA ENGINEERING
Untuk kamu yang sudah memahami bahasa pemrograman Python
- Introduction to Data Engineering and Pentaho: This module provides a foundational understanding of data engineering principles and introduces the Pentaho platform for data integration and processing.
- Data Ingestion and Integration: Explore diverse data sources and learn how to seamlessly integrate them using Pentaho, enabling effective data flow across systems.
- Data Cleaning and Data Transformation: Discover techniques for improving data quality and transforming information for analysis, utilizing both Pentaho and Python to enhance data integrity.
- Data Processing: Delve into the world of data processing, orchestrating workflows and utilizing Pentaho alongside Python to create efficient pipelines for complex data operations.
MACHINE LEARNING FUNDAMENTALS FOR FINANCE
Untuk kamu yang sudah memahami Python dan Data Engineering
- Introduction to Machine Learning: This subcourse covers the fundamentals of Machine Learning, an approach where algorithms enable systems to learn from data and improve their performance over time.
- Types of Machine Learning: This segment provides an introduction to three fundamental paradigms: Supervised Learning, Unsupervised Learning, and Semi-Supervised Learning.
FRAUD DETECTING USING MACHINE LEARNING
Untuk kamu yang sudah memahami Python, Data Engineering, dan Machine Learning
- Introduction to Fraud Detection
- Data Pre-Processing: Reducing Memory Dataset; Merging Dataframes; Defining Variable; Data Cleaning; Data Transformation; Handling Imbalanced Dataset
- Building Machine Learning Model
- Evaluate Machine Learning Model: Confusion Matrix; Accuracy, Precision, Recall, F1 Score; AUC ROC Score; Code Example
- Hyperparameter Tuning Model
LOAN APPLICATION RATING
Untuk kamu yang sudah memahami Python, Data Engineering, Machine Learning, dan Fraud Detecting dengan Machine Learning.
- Introduction to Loan Application Rating
- Data Pre-Processing & Feature Selection : Load Dataset; Defining Variable; Data Cleaning; Data Transformation; Feature Selection
- Building Machine Learning Model
- Evaluate Machine Learning Model : Precision, Accuracy, Recall, F1 Score, ROC AUC Score; Confusion Matrix
- Hyperparameter Tuning Model
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