Courses
Machine Learning for Business Intelligence (MLBI)
In this course, we introduce to 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.
Upcoming Training Dates
8 - 9 October 2024
* This training schedule is subject to change
Pass the 2-hour exam consisting of 50 Online Multiple Choice Questions with the score of 70% to earn this certification
One of the most industry validated digital skills certification in Asia. Course and exams is taken by the industries / academia / governments from 26 countries in Asia via 30+ Authorised Training Partners (ATP) and 50+ Authorised Academy Partners (AAP).
“Vendor-neutral” certifications refer to any certifications that are not directly associated with specific IT vendors. These certifications tend to develop a knowledge and skill base that is universally applicable and individual with skills that are more conceptual, setting you up to work with a greater range of products / tools.
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Applicable in any industry, machine learning tops as a new wave of technology. More and more businesses are starting to invest in machine learning, as it potentially raises 2 to 5 times the ROI. Knowing about machine learning enables you to identify sales patterns and forecast demands.
Some of the most versatile skills of this age has got something to do with Artificial Intelligence(AI). Like AI, Machine learning entirely dismisses human intervention in its processes and functions, providing accurate results from data. This is how enterprise security can be enhanced.
By attending this 2-day instructor-led course on Machine Learning for Business Intelligence, you will be able to understand real-life applications of machine learning in present contexts. With wide opportunities in the machine learning realm, you can easily tap into the market by equipping yourself with it’s processes and tools.
- Understanding of the basic concepts and practical applications of Machine Learning algorithms
- Capability to identify the long-term impact of ML to businesses
- Skills to apply ML algorithms to their own real-world problems
- Explain machine learning concepts & describe applications of well-known machine learning algorithms
- Apply machine learning techniques to a list of practical problems
Training Module
Part I: The Machine Learning Workflow
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
Real-world data
- Data collection
- Pre-processing data for modeling
- Using data visualization
Modeling and prediction
- Basic machine learning modeling
- Classification
- Regression
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
Basic feature engineering
- Why is Feature Engineering Useful?
- Basic feature engineering process
- Feature selection
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Part II: Practical Applications
Example: NYC taxi data
- Data visualization and preparation
- Modeling
Advanced feature engineering
- Advanced text features
- Image features
- Time-series features
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
Scaling machine-learning workflows
- Before scaling up
- Scaling Machine learning modeling pipelines
- Scaling predictions
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
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