# Pathway - Supervised ML and Analytics
# ML Analytics Pathway Introduction
# M5 Exploratory Data Analysis (EDA)
- ML workflow
- Basic statistical measures (mean, median, mode)
- Data distribution analysis (histograms, box plots)
- Correlation analysis (scatter plots, correlation matrices)
- Identifying outliers and anomalies
- Using libraries like Pandas, Matplotlib, and Seaborn for EDA
- Creating insightful visualizations (bar charts, heatmaps, pair plots)
- Drawing initial insights from the data
# Module 6 - Data Preparation
- Data cleaning techniques
- Handling missing data (imputation strategies)
- Dealing with categorical data (one-hot encoding)
- Feature scaling and normalization
- Handling outliers (when to remove, when to keep)
- Handling class imbalance
# Module 8 - Machine Learning Algorithms
- Train / Test / Val
- Linear / Logistic Regression
- Decision Trees
- Random Forests / Boosting vs Bagging
- Understanding Accuracy and the Confusion Matrix
# Module 9 - Model Training and Evaluation
- Cross-validation
- Overfitting / underfitting and handling each
- Learning and validation curves
# Module 10 - Model Improvements
- Ensemble methods
- Voting classifiers
- Stacking
- Blending
- Hyperparameter tuning
- Grid search
- Random search
- Bayesian optimization
- Error analysis and iterative improvement
# Module 11 - Model Deployment
- Creating a simple API for model deployment (using Flask)
- Monitoring model performance in production
- Ethical considerations in AI/ML
- Bias and fairness in machine learning
- Privacy concerns and data protection
- Transparency and explainability of models
- Responsible AI practices
- Legal and regulatory considerations (e.g., GDPR, CCPA)
- Strategies for ongoing model monitoring, maintenance, and improvement