Machine Learning Practitioner Certification

Course Overview

This intermediate-level course spans 4 weeks and requires approximately 45 hours of commitment. The total cost is $495, with a hybrid delivery format that combines self-paced video content and interactive labs. Prerequisites include proficiency in Python and a foundational understanding of statistics.

Course Framework

Week 1: Building the Foundation

• Topics Covered:
  • Full Machine Learning lifecycle
  • Data preprocessing techniques
  • Handling missing values
  • Correlation analysis
  • Feature encoding methods
• Tools Used: NumPy, Pandas, Scikit-learn
• Lab Task: Clean and prepare a dataset for training.
• Assessment: 10-item quiz on ML workflow concepts and evaluation metrics.

Week 2: Supervised Learning

• Topics Covered:
  • Regression techniques (linear, ridge, lasso)
  • Classification methods (logistic regression, decision trees, random forests)
• Hands-on Activities: Code demos integrating cross-validation and hyperparameter tuning.
• Project: Predict housing prices or customer churn using real data.
• Evaluation: Auto-graded quiz and peer-assessed mini-project rubric.

Week 3: Unsupervised Learning and Optimization

• Topics Covered:
  • Clustering techniques (K-means, hierarchical)
  • Dimensionality reduction methods (PCA, t-SNE)
  • Feature selection techniques
• Focus: Visualization and interpretation of results.
• Lab Task: Perform clustering and reduce features for visual insight.
• Assessment: 10-question quiz and auto-evaluated code submission to verify clustering accuracy.

Week 4: Deployment, Explainability, and Ethics

• Topics Covered:
  • Deployment pipelines (Flask, Docker)
  • Model versioning (MLflow)
  • Monitoring model drift
  • Ethical AI principles (bias detection, transparency, fairness)
• Capstone Project: An end-to-end ML project integrating data preprocessing, model training, deployment of a mock API, and fairness analysis.
• Final Assessment: Certification exam and capstone evaluation rubric.

Automation and Deliverables

This course includes LMS integration for quizzes, lab tracking, and auto-grading. Upon completion, learners will receive a blockchain-verified credential, provided they achieve a passing score of 70% or higher. Deliverables include instructional videos, datasets, Jupyter notebooks, quizzes, and final project instructions.

Next Steps

The Machine Learning Practitioner Certification is now ready for deployment. Enroll today to advance your machine learning skills!