Course Overview
- Course Title: Certified Supervised Machine Learning
- Instructor: Muhammad Shafiq (Data Scientist | AI & ML Engineer | Lecturer | Researcher)
- Target Audience:
- Aspiring data scientists and AI/ML engineers
- Professionals seeking predictive modeling skills
- Students preparing for machine learning certifications
- Developers transitioning into AI-driven applications
- Prerequisites:
- Basic understanding of Python programming
- Familiarity with statistics and linear algebra (recommended)
Curriculum Highlights
- Key Topics Covered:
- Foundational concepts of supervised learning (training, validation, testing, generalization)
- Regression models: Linear Regression, Polynomial Regression, Ridge, Lasso, Elastic Net
- Classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Naive Bayes
- Tree-based models: Decision Trees, Random Forests, Gradient Boosting (XGBoost, LightGBM)
- Model evaluation: Metrics for regression/classification, cross-validation, hyperparameter tuning
- Real-world projects: Hands-on case studies for portfolio development
- Key Skills Learned:
- Implementing and optimizing supervised machine learning algorithms
- Data preprocessing and feature engineering
- Hyperparameter tuning for model performance
- Model evaluation using industry-standard metrics
- Deploying predictive models for real-world applications
Course Format
- Duration: N/A (Self-paced with 3 practice tests)
- Format: Online, self-paced with mobile access
- Resources:
- Practice tests (3 included)
- Mobile-compatible learning materials
- Project-based assignments (real-world datasets)