Machine Learning for Business Intelligence

In this course, we introduce the field of machine learning and describe the well-known processes, algorithms, and tools for one to be a successful machine learning practitioner. This course will help to build skills in data acquisition and modeling, classification, and regression. In addition, one will also get to explore very important tasks such as model validation, optimization, scalability, and real-time streaming.

Who Should Attend?

Anyone who is keen to learn more in-depth about Machine Learning and the real applications of Machine Learning for Business Intelligence today.

Course Outline

Part I: The Machine Learning Workflow

1.1 What is machine learning?

• How Machines Learn
• Using Data to Make Decisions
• The Machine Learning Workflow: from Data to Deployment
• Boosting Model Performance with Advanced Techniques

1.2 Real-world data

• Data collection
• Pre-processing data for modeling
• Using data visualization

1.3 Modeling and prediction

• Basic machine learning modeling
• Classification
• Regression

1.4 Model evaluation and optimization

• Model generalization: evaluating predictive accuracy for new data
• Evaluation of classification models
• Evaluation of regression models
• Model Optimization through Parameter Tuning

1.5 Basic feature engineering

• Why is Feature Engineering Useful?
• The basic feature engineering process
• Feature selection

Part II: Practical Applications

2.1 Example: NYC taxi data

• Data visualization and preparation
• Modeling

2.2 Advanced feature engineering

• Advanced text features
• Image features
• Time-series features

2.3 Advanced Natural Language Processing (NLP) example: movie review sentiment

• Exploring data and use case
• Extracting basic NLP features and building the initial model
• Advanced algorithms and model deployment considerations

2.4 Scaling machine-learning workflows

• Before scaling up
• Scaling Machine learning modeling pipelines
• Scaling predictions

2.5 Example: digital display advertising

• Digital Advertising
• Digital Advertising Data
• Feature Engineering and Modeling Strategy
• Size and Shape of Data
• Singular Value Decomposition
• Resource Estimation and Optimization
• Modeling
• K-nearest neighbors
• Random forests
• Other Real Word Considerations
  

Course Objectives

Introducing the basic concepts and practical applications of Machine Learning algorithms.

Providing students with the capabilities to:

• identify the long-term impact of machine learning to businesses;
• apply machine learning algorithms to their own real-world problems.

Learning Outcomes

Students will be able to:

• Explain machine learning concepts & describe applications of well-known machine learning algorithms
• Apply machine learning techniques to a list of practical problems
  

Prerequisites

Anyone working with Business Intelligence and Data Analysis.