Machine Learning With Business Applications
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Machine learning algorithms are part of artificial intelligence (AI) that imitates the human learning process. Machines are more powerful than the human brain at analysing data and gain insights about the business. Machine Learning (ML) algorithms have applications across various industries and different functional areas. The primary objective of ML is to assist in decision making. Today ML is used for driving innovation and as competitive strategy by several organizations.
Decision making and problem solving has become complex due to competition and scale of operations of firms. The theory of bounded rationality proposed by Nobel Laureate Herbert Simon is evermore significant today with increasing complexity of the business problems; limited ability of human mind to analyze alternative solutions and the limited time available for decision making. Introduction of Enterprise Resource Planning (ERP) systems has ensured availability of data in many organizations; however, traditional ERP systems lacked data analysis capabilities that can assist the management in decision making. Machine learning algorithms are a set of techniques and heuristics that can be used to analyse data to improve business performance through fact-based decision-making. In the recent past, automation and innovation across several industries has been driven by machine learning algorithms.
Several reports claim that AI and machine learning specialists in Silicon Valley with few years of experience are paid $300,000 - $500,000 a year. Bernard Marr in his article published in the Forbes magazine claimed that 74% of the customers will be happy to receive computer generated insurance advice. While using machine learning algorithms, we develop several models which can run into several hundreds and each model is treated as a learning opportunity. Machine learning algorithms are classified into the following four categories: 1. Supervised Learning Algorithms, 2. Unsupervised Learning Algorithms, 3. Reinforcement Learning Algorithms, and 4. Evolutionary Learning Algorithms. In this management development we will be discussing various machine learning algorithms with their applications using case studies from various industries
The course is designed to provide in-depth knowledge of Machine Learning Algorithms that can be used for fact-based decision-making using real case studies and understand how machine learning algorithms are used for automation and innovation. Primary objectives of the course are:
- Understand various machine learning algorithms such as supervised, unsupervised and reinforcement algorithms.
- Learn to analyse data to gain insights using an appropriate machine learning algorithm under a given business context.
- Learn various supervised learning algorithms such as regression, logistic regression, decision tree learning, random forest, boosting, neural networks and deep learning algorithms with applications in solving managerial problem.
- Learn unsupervised learning algorithms such as k-means clustering, factor analysis, multivariate Gaussian distribution and its applications in gaining insights from data.
- Understand how reinforcement and evolutionary algorithms are used by organizations.
- Understand applications of ML in functional areas such as marketing, finance, operations and supply chain and HR.
- Analyse and solve problems from different industries such as e-commerce, insurance, manufacturing, service, retail, software, banking and finance, sports, pharmaceutical, aerospace etc using ML algorithms.
- Hands on experience with software such as Microsoft Excel, Evolver, R, Python and other proprietary software.
Supervised Learning Algorithms with Applications in Predictive Analytics:
- Simple linear regression: coefficient of determination, significance tests, residual analysis, confidence and prediction intervals. Multiple linear regression (MLR): coefficient of multiple coefficient of determination, interpretation of regression coefficients, categorical variables, heteroscedasticity, multicollinearity, outliers, auto-regression and transformation of variables. MLR model development and feature selection.
Supervised Learning Algorithms with Applications in Classification Problems:
- Logistic and Multinomial Regression: Logistic function, estimation of probability using logistic regression, Deviance, Wald test, Hosmer Lemeshow test. Naïve Bayes Algorithm. Feature selection in logistic regression. Ensemble Methods – Random Forest and Boosting
Supervised Learning Algorithms for Forecasting:
- Moving average, exponential smoothing, Trend, cyclical and seasonality components, ARIMA (autoregressive integrated moving average) and ARIMAX models.
- Application of Supervised Learning Algorithms in retail, direct marketing, health care, financial services, insurance, supply chain etc
Unsupervised Learning Algorithms
- Clustering: K-means Clustering and Hierarchical Clustering; Data Reduction Techniques: Factor Analysis; Anomaly detection: Multivariate Gaussian Distribution
Reinforcement Learning Algorithms
Markov Chains, Markov Decision Process, Policy Iteration and Value Iteration Algorithms with applications in marketing and finance
The following case studies will be discussed during the course.
- Predicting Net Promoter Score to Improve Patient Experience at Manipal Hospitals
- 1920 Evil Returns – Bollywood and Social Media Marketing
- Breaking Barriers – Micro mortgage Analytics
- Consumer Analytics at Big Basket – Product Recommendations
- Customer Analytics at Flipkart.Com
- Forecasting Demand for Food at Apollo Hospitals
- HR Analytics at Scaleneworks – Behavioural Modelling to Predict Renege
- Predicting Earnings Manipulations by Indian Firms Using Machine Learning Algorithms
- Machine Learning Algorithms to Drive CRM in the online E-commerce site at VMWare
- Consumer choice between house brands and national brands in detergent purchases at Reliance Retail
Who should attend
Managers and decision makers with roles in analytics and AI based consulting in marketing, operations, supply chain management, finance, insurance and general management in various industries should attend the course. The course is suitable for those who are already working on Machine Learning to enhance their knowledge as well as for those with analytical aptitude and would like to start a new career in Analytics.