- 3 Sections
- 16 Lessons
- Lifetime
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- Section-1: Customer Churn AnalysisPredict which customers are likely to leave11
- 1.1PDSML Day-1 👉 Making the Environment Ready85 Minutes
- 1.2PDSML Day-2 👉 Importing Libraries and Dataset90 Minutes
- 1.3PDSML Day-3 👉 Data Preprocessing (Part-1)90 Minutes
- 1.4PDSML Day-4 👉 Data Preprocessing (Part-2)90 Minutes
- 1.5PDSML Day-5 👉 Data Preprocessing (Part-3)90 Minutes
- 1.6PDSML Day-6 👉 Data Preprocessing (Part-4)60 Minutes
- 1.7PDSML Day-7 👉 What is Logistic Regression?70 Minutes
- 1.8PDSML Day-8 👉 Training the Baseline Model for Churn Analysis90 Minutes
- 1.9PDSML Day-9 👉 Churn Analysis Model Evaluation (Part-1)90 Minutes
- 1.10PDSML Day-10 👉 Churn Analysis Model Evaluation (Part-2)90 Minutes
- 1.11PDSML Day-11 👉 Exploratory Descriptive Analysis on Churn Data90 Minutes
- Section-2: Customer SegmentationSegment customers based on spending patterns.4
- 2.1PDSML Day-12 👉 Customer Segmentation Data Preprocessing90 Minutes
- 2.2PDSML Day-13 👉 K-Means Clustering Intuition and the Elbow Method70 Minutes
- 2.3PDSML Day-14 👉 Finding the Optimum Number of Clusters80 Minutes
- 2.4PDSML Day-15 👉 Building the Clustering Model and Finalizing Clusters90 Minutes
- Section-3: Fake News DetectionClassify whether a news article is real or fake.1
PDSML Day-15 👉 Building the Clustering Model and Finalizing Clusters
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