Machine Learning Courses and Certifications

Coursera

Engineer & Explain AI Model Decisions

Discover how to build trustworthy AI systems by mastering feature engineering, model interpretability, and bias detection for ethical deployment in production environments.

• Coursera
• 3 hours 47 minutes
• Self-Paced
• Paid Course

SAS Certification Practice Exams

Master SAS certification readiness with three practice exams covering applied statistics, machine learning using Viya, and ModelOps for comprehensive exam preparation.

• Coursera
• 3 hours
• Self-Paced
• Paid Course

Coursera

Automate, Analyze, and Evaluate ML Experiments

Unlock ML experiment automation, bias detection, and A/B testing to improve model accuracy and measure real business impact through statistical validation.

• Coursera
• 2 hours 39 minutes
• Self-Paced
• Paid Course

Coursera

Choose Cost-Effective ML Algorithms Fast

Learn to evaluate ML algorithms by resource utilization, benchmark XGBoost vs Random Forest, and select cost-effective models that are efficient, predictable, and production-ready.

• Coursera
• 1 hour 59 minutes
• Self-Paced
• Paid Course

Packt

Mastering NLP: Foundations, ML & LLMs

Explore NLP from fundamentals to advanced LLMs, covering ML, deep learning, RAG pipelines, and LangChain to design and deploy cutting-edge AI language systems.

• Coursera
• 11 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera

Evaluate, Analyze, and Model Performance

Master ML model evaluation using RMSE, MAE, confusion matrices, and bootstrapping to diagnose failures, validate improvements, and make evidence-based decisions confidently.

• Coursera
• 2 hours 51 minutes
• Self-Paced
• Paid Course

Coursera

Engineer AI Models: Explain, Tune & Experiment

Master AI model engineering through feature tuning, explainability methods like SHAP/LIME, and structured experimentation to transform black box models into trusted, business-ready solutions.

• Coursera
• 2 hours 21 minutes
• Self-Paced
• Paid Course

H2O.ai

H2O Cloud AI Developer Services

Discover how to build, deploy, and scale AI solutions using H2O Cloud's comprehensive suite of services for tabular data, NLP, computer vision, and AI-assisted development.

• Coursera
• 57 minutes
• Self-Paced
• Paid Course

Coursera

Learn to Choose the Right ML Model

Discover how to systematically select and deploy ML models using structured problem typing, algorithm comparison, and metrics-driven workflows for production success.

• Coursera
• 2 hours 45 minutes
• Self-Paced
• Paid Course

Coursera

Reproduce and Evaluate AI Research Workflows

Master reliable machine-learning experiments through controlled ablation studies, environment versioning, and transparent documentation for reproducible AI research workflows.

• Coursera
• 1 hour 46 minutes
• Self-Paced
• Paid Course

Coursera

Validate, Analyze, and Monitor ML Models

Master ML model validation, A/B testing, shadow deployments, and drift monitoring using PSI to ensure safe releases and long-term model health in production environments.

• Coursera
• 2 hours 45 minutes
• Self-Paced
• Paid Course

Coursera

Build Predictive & Supervised Models

Master production-ready machine learning workflows with random forest models, automated monitoring, and reproducible pipelines for reliable business forecasting.

• Coursera
• 3 hours 40 minutes
• Self-Paced
• Paid Course

Coursera

AppDynamics Monitoring

Master AppDynamics monitoring for ML applications with hands-on implementation, advanced diagnostics, and optimization techniques for production systems.

• Coursera
• 3 hours 56 minutes
• Self-Paced
• Paid Course

Coursera

Measure Vector Similarity

Master cosine similarity, dot-product, and Euclidean distance through hands-on Python implementation and real-world benchmarking for ML applications.

• Coursera
• 1 hour 49 minutes
• Self-Paced
• Paid Course

Coursera

Improve Accuracy with ML Ensemble Methods

Master ensemble techniques like bagging, boosting, and random forests to enhance ML model accuracy and reliability using Java in hands-on labs and real-world projects.

• Coursera
• 3 hours 55 minutes
• Self-Paced
• Paid Course