### Course Overview
- **Course Title:** Certified Anomaly Detection & Outlier Analytics
- **Instructor:** **Muhammad Shafiq** (Data Scientist, AI & ML Engineer, Lecturer, Researcher)
- **Target Audience:**
- Aspiring **data scientists** and **machine learning engineers**
- Professionals in **fraud detection**, **cybersecurity**, or **predictive maintenance**
- Analysts seeking expertise in **outlier analytics** and **anomaly detection**
- Python programmers transitioning into **applied AI/ML**
- **Prerequisites:**
- Basic **Python programming** knowledge
- Familiarity with **machine learning fundamentals** (recommended but not mandatory)
### Curriculum Highlights
- **Key Topics Covered:**
- **Supervised, unsupervised, and semi-supervised anomaly detection** techniques
- **Isolation Forest (iForest)**, **Local Outlier Factor (LOF)**, and **One-Class SVM (OC-SVM)**
- **Time-series anomaly detection** with deep learning methods
- **Data preprocessing** and **feature engineering** for outlier problems
- **Handling class imbalance** in anomaly detection datasets
- **Model deployment** for production-level applications
- **Real-world case studies**:
- **Credit card fraud detection**
- **Industrial equipment failure prediction**
- **Key Skills Learned:**
- Implementing **Python-based anomaly detection** using **Scikit-learn** and **PyOD**
- Building and evaluating **machine learning models** for outlier analytics
- Interpreting and validating **anomaly detection results**
- Applying **advanced algorithms** to real-world datasets
- Deploying models in **industry-standard workflows**
### Course Format
- **Duration:**
- **3 practice tests** (hands-on assessments)
- **Self-paced** (lifetime access to materials)
- **Format:**
- **Project-based certification** with practical exercises
- **Mobile-accessible** content
- **Resources:**
- **Real-world datasets** for case studies
- **Downloadable code templates** and Python scripts
- **Quizzes** for skill validation
### Special Offer (If Applicable)
- **Limited Time Coupon Code:** N/A
