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Course Outline
Introduction to Machine Learning in Business Operations
- The role of machine learning as a fundamental element of the broader Artificial Intelligence framework
- Categorization of learning paradigms: supervised, unsupervised, reinforcement, and semi-supervised models
- Algorithmic methodologies commonly deployed in enterprise operational contexts
- Analysis of strategic risks, operational challenges, and applicable use cases for ML within AI ecosystems
- Methodological approaches to addressing model overfitting and managing the bias-variance tradeoff
Methodologies and Operational Workflows for Machine Learning
- The complete machine learning lifecycle: from problem identification through to production deployment
- Core analytical techniques: classification, regression, clustering, and anomaly detection
- Criteria for selecting between supervised and unsupervised learning approaches in organizational contexts
- Application of reinforcement learning principles to automate and optimize business processes
- Governance and accountability considerations in ML-driven decision support systems
Data Preparation and Feature Engineering for Government and Business Use
- Data management protocols: ingestion, quality assurance, and structural transformation
- Feature engineering strategies: encoding schemes, data transformation, and variable creation
- Standardization of feature scales: normalization and standardization techniques
- Dimensionality reduction methods: Principal Component Analysis (PCA) and variable selection
- Exploratory data analysis and the creation of visualizations for stakeholders
Neural Networks and Deep Learning Architectures
- Foundational concepts of neural networks and their integration into business analytics
- Network architecture components: input layers, hidden layers, and output structures
- Optimization techniques: backpropagation mechanisms and activation functions
- Application of neural architectures to classification and regression objectives
- Utilization of deep learning for complex forecasting and advanced pattern recognition
Sales Forecasting and Predictive Analytics for Strategic Planning
- Comparative analysis of time series modeling versus regression-based forecasting approaches
- Decomposition of temporal data: identifying trends, seasonality, and cyclical patterns
- Application of statistical methods: linear regression, exponential smoothing, and ARIMA models
- Deployment of neural networks for modeling nonlinear forecast relationships
- Practical application: Modeling and projecting monthly sales volume
Applied Case Studies in Business and Public Sector Operations
- Enhancing prediction accuracy through advanced feature engineering in linear regression models
- Customer segmentation analysis utilizing clustering algorithms and self-organizing maps
- Retail intelligence: Market basket analysis and association rule mining
- Risk assessment and classification of customer default using logistic regression, decision trees, XGBoost, and SVM
Summary and Implementation Roadmap
Requirements
- Foundational knowledge of machine learning principles and their practical applications
- Proficiency in spreadsheet environments or standard data analysis tools
- Basic exposure to Python or other programming languages is advantageous, though not required
- Professional interest in leveraging machine learning for real-world business and forecasting challenges
Target Audience
- Business analysts and strategists
- Artificial Intelligence and data science professionals
- Data-driven decision makers, managers, and public sector leaders
21 Hours
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
