Advanced Certificate in Machine Learning for Customer Churn Prediction
-- viewing nowThe Advanced Certificate in Machine Learning for Customer Churn Prediction is a crucial course for professionals seeking to excel in the data science field. This certification focuses on predictive analytics, a key driver of business growth, enabling learners to identify customers at risk of churn, reducing costs, and increasing revenue.
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Course Details
• Introduction to Machine Learning – Understanding the basics of machine learning, including supervised and unsupervised learning, regression, classification, and clustering.
• Data Preprocessing – Cleaning, transforming, and preparing data for machine learning models, including handling missing data, outliers, and categorical variables.
• Feature Selection and Engineering – Identifying and creating relevant features to improve model performance, including domain-specific knowledge and feature importance.
• Supervised Learning Algorithms – Learning various supervised learning algorithms, such as logistic regression, decision trees, random forests, and support vector machines.
• Unsupervised Learning Algorithms – Learning various unsupervised learning algorithms, such as k-means clustering, hierarchical clustering, and principal component analysis.
• Model Evaluation – Evaluating model performance using appropriate metrics, such as accuracy, precision, recall, F1 score, and ROC curve.
• Hyperparameter Tuning – Optimizing model performance through hyperparameter tuning techniques, such as grid search and random search.
• Time Series Analysis – Understanding time series data, including trends, seasonality, and stationarity, and applying appropriate models, such as ARIMA and SARIMA.
• Customer Churn Prediction – Applying machine learning algorithms to predict customer churn, including feature engineering, model evaluation, and hyperparameter tuning.
• Deployment and Monitoring – Deploying machine learning models in production environments, including containerization, cloud computing, and monitoring model performance.
Career Path
Entry Requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course Status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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