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IBM C1000-177 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Selection, Training, Evaluation, and Presentation | 17% | - Choose appropriate model metrics to assess model performance - Split the data to support model evaluation - Identify adequate machine learning model |
| Topic 2: Pre-Processing and Feature Engineering | 33% | - Normalize data - Mitigate imbalanced data - Select relevant features - Handle data anomalies and missing values - Transform features - Integrate data from different sources and formats - Identify the best categorical data encoding techniques |
| Topic 3: Evaluate the Business Problem | 16% | - Formulate the hypothesis to be tested - Translate business objectives into Data Science/ML/AI solutions - Identify appropriate tools for analysis |
| Topic 4: Perform Exploratory Data Analysis | 21% | - Deselect features that have minimal predictive value - Visualize data to identify patterns/trends - Visually examine the data for data understanding - Assess data characteristics to guide future processing - Conduct statistical analysis of data |
| Topic 5: Development Tools and Techniques | 13% | - Assess which modeling and statistical techniques are best suited - Select the appropriate environment and libraries |
