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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.
Part I: The Machine Learning Workflow
1.1 What is machine learning?
1.2 Real-world data
1.3 Modeling and prediction
1.4 Model evaluation and optimization
1.5 Basic feature engineering
Part II: Practical Applications
2.1 Example: NYC taxi data
2.2 Advanced feature engineering
2.3 Advanced Natural Language Processing (NLP) example: movie review sentiment
2.4 Scaling machine-learning workflows
2.5 Example: digital display advertising
Introducing the basic concepts and practical applications of Machine Learning algorithms.
Providing students with the capabilities to:
Students will be able to:
Anyone working with Business Intelligence and Data Analysis.
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