Applied Machine Learning with Python Certification

Module 1: Introduction to Machine Learning

• Machine learning fundamentals
• AI vs Machine Learning
• Types of machine learning
• Business applications
• Machine learning lifecycle

Module 2: Python for Machine Learning

• Python fundamentals
• Jupyter Notebook
• NumPy
• Pandas
• Python best practices

Module 3: Data Preparation

• Data collection
• Data cleaning
• Missing value handling
• Data transformation
• Data preprocessing

Module 4: Exploratory Data Analysis

• Data visualization
• Statistical analysis
• Correlation analysis
• Pattern identification
• Feature exploration

Module 5: Feature Engineering

• Feature selection
• Feature creation
• Encoding categorical variables
• Feature scaling
• Dimensionality reduction concepts

Module 6: Supervised Learning

• Regression algorithms
• Classification algorithms
• Decision Trees
• Random Forest
• K-Nearest Neighbors

Module 7: Unsupervised Learning

• Clustering
• K-Means
• Hierarchical clustering
• Dimensionality reduction
• Anomaly detection

Module 8: Model Training

• Training datasets
• Validation datasets
• Testing datasets
• Model fitting
• Training workflows

Module 9: Model Evaluation

• Performance metrics
• Accuracy
• Precision and Recall
• F1 Score
• ROC-AUC

Module 10: Hyperparameter Tuning

• Grid Search
• Random Search
• Cross-validation
• Model optimization
• Performance comparison

Module 11: Ensemble Learning

• Bagging
• Boosting
• Voting classifiers
• Model ensembles
• Performance improvement

Module 12: Scikit-learn

• Scikit-learn workflow
• Pipelines
• Model selection
• Utility functions
• Best practices

Module 13: Model Deployment

• Saving trained models
• Model serialization
• REST API concepts
• Deployment workflows
• Production considerations

Module 14: Machine Learning Projects

• Project planning
• Dataset selection
• Workflow management
• Documentation
• Project structure

Module 15: Data Visualization

• Matplotlib
• Seaborn
• Interactive visualization concepts
• Result presentation
• Reporting

Module 16: Model Monitoring

• Performance monitoring
• Model drift
• Data drift
• Retraining strategies
• Continuous improvement

Module 17: Responsible Machine Learning

• Model fairness
• Bias detection
• Explainability
• Data privacy
• Ethical AI practices

Module 18: Performance Optimization

• Efficient model training
• Feature optimization
• Resource utilization
• Computational efficiency
• Scalability

Module 19: End-to-End Machine Learning Workflow

• Business problem definition
• Data preparation
• Model development
• Evaluation
• Deployment planning

Module 20: Applied Machine Learning Case Studies

• Predictive analytics
• Customer segmentation
• Sales forecasting
• Recommendation systems
• Business problem solving