Principles of Machine Learning and Data Modeling
- Data Structures and Types of Variables
- Supervised vs. Unsupervised Machine Learning Modeling
- Data Preparation Techniques
- Feature Engineering
- Evaluation of Machine Learning Models
- Optimizing Machine Learning Models
- Ensemble Learning
- Common Mistakes in Modeling
Regression Modeling
- Concepts and Definitions
- Performance Metrics
- Linear Regression
- Generalized Linear Models (GLM)
Classification Modeling
- Concepts and Definitions
- Performance Metrics
- Logistic Regression
- k-Nearest Neighbor (k-NN)
- Naïve Bayes
- Decision Trees (applied to Regression as well)
- Random Forrest (applied to Regression as well)
- Gradient Boosted Machines (applied to Regression as well)
- Support Vector Machines (applied to Regression as well)
- Neural Networks (applied to Regression as well)
Recommendation Systems
- Concepts and Definitions
- Performance Metrics
- Apriori algorithm for association data mining
Time Series Analysis
- Concepts and Definitions
- Performance Metrics
- Stationarity, causality, and invertibility
- Autoregressive Integrated Moving Average (ARIMA)
Graph Analytics
- Concepts and Definitions
- Centrality and Connectivity Measures
- Application to Social Network Analysis
Text Analytics
- Concepts and Definitions
- Feature Extraction
- Topic Modeling
- Sentiments Analysis
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