Machine Learning Courses and Certifications

Packt

Building, Evaluating, and Operationalizing ML Models

Master the complete ML lifecycle from data exploration to deployment using Azure ML Studio, covering algorithm selection, model evaluation, and automated pipelines.

• Coursera
• 7 hours 49 minutes
• Self-Paced
• Paid Course

EDUCBA

Analyze & Build a Churn Prediction Model in R

Master customer retention analytics by building and evaluating churn prediction models in R using logistic regression, data preprocessing, and performance metrics like ROC curves.

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

Coursera

Model Training & Evaluation

Master machine learning model training and evaluation through PyTorch practice, exploring mini-batch training, learning-rate schedulers, loss curve analysis, and class-imbalance techniques.

• Coursera
• 2 hours 35 minutes
• Self-Paced
• Paid Course

Coursera Career Certificate

ML Model Training & Validation

Master end-to-end ML model training, validation, and optimization for production environments through hands-on labs covering data preparation, algorithm comparison, and deployment.

• Coursera
• 4 weeks, 10 hours/week
• Self-Paced
• Paid Course

Coursera

Architecting and Integrating Scalable AI Systems

Master the design of end-to-end AI architectures, integrating ML models into production using cloud platforms, APIs, and monitoring tools to build scalable, reliable systems.

• Coursera
• 10 hours 42 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

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

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

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

Optimize AI: Build Fast Efficient Pipelines

Master building fast, efficient AI training and inference pipelines by optimizing data loading, computational graphs, and GPU utilization to reduce latency and boost performance.

• Coursera
• 1 hour 28 minutes
• 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

Coursera

ML Concepts, Models & Workflow Essentials

Master advanced machine learning in Java using Weka, Deeplearning4j, and Smile to build enterprise-grade predictive models, customer segmentation, and scalable MLOps solutions.

• Coursera
• 4 hours 36 minutes
• 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