AI & Data • Level: Intermediate
Applied Machine Learning & Anomaly Detection
From data cleaning and SMOTE class balancing to deploying scikit-learn classifiers inside RESTful APIs for real-time predictions.
Duration10 Weeks
Total Lessons36 Lessons
Skill LevelIntermediate
EnrollmentOpen Cohort
Course Overview
Bridge raw data science with production software engineering. Learn how to train classification models, balance skewed security datasets, interpret ROC-AUC metrics, and wrap models in high-performance inference APIs.
Curriculum Breakdown
1Module 1: Data Preprocessing & Class Imbalance
- Feature selection, normalization, and one-hot encoding
- Handling severe imbalance using SMOTE and Tomek links
2Module 2: Supervised Classification Algorithms
- Logistic Regression, Decision Trees, Random Forests, SVM, KNN, Naive Bayes
- Hyperparameter tuning using GridSearchCV and cross-validation
3Module 3: Model Evaluation in High-Stakes Domains
- Precision vs Recall trade-offs in cybersecurity anomaly detection
- Confusion matrices, ROC curves, and false-positive minimization
4Module 4: Model Deployment & API Serving
- Serializing models with Joblib and Pickle
- Building low-latency prediction endpoints with Flask and FastAPI
Prerequisites & Requirements
- Python proficiency
- High school algebra & statistics
Lead Instructor
AK
Aizaz Khalid
Founder & AI/ML Specialist
Guiding you through practical, real-world application architecture and defense strategies directly from client deployments.
Join Upcoming Cohort
Enrollment includes access to recorded lesson modules, practical lab exercises, and mentor code reviews.
Explore Machine Learning TrackCovered Stack
PythonScikit-learnPandasNumPySMOTEFlaskFastAPI
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