Advanced Analytics and Machine Learning using Python (SF) (Synchronous e-Learning)
About This Course
• Evaluate the business problems in the financial industry today and the data associated with them to develop predictive analytics applications
• Understand and use the various algorithms and tools to work with the data and test hypotheses
• Construct analytics models/results as part of solutions to address business problems and derive patterns and solutions
• Learn to code machine learning algorithms and build models using Python
• Evaluate the performance of analytics models and learn how to optimize models
• Learn the various statistical models, machine learning algorithms, and understand how to use them in various business scenarios
• Evaluate the importance of data and features which are used to solve business problems
• Analyze the results or outputs of analytics models
• Understand and evaluate possible big data applications in conjunction with machine learning
What You'll Learn
Throughout the course, students will explore various machine learning algorithms, such as decision trees, support vector machines, and others, and learn how to implement them in Python using popular libraries. In addition to theoretical concepts, the course emphasizes on the practical applications of advanced analytics and machine learning in real-world scenarios. Participants will work on various case studies, including data preprocessing, feature engineering, model selection, and evaluation.
Upon completion of the course, students will be able to confidently apply advanced analytics and machine learning techniques to a wide range of data-driven problems.
Entry Requirements
This course requires a basic understanding of Python. Participants who do not have the basic knowledge are encouraged to take up Fundamentals of Python Programming prior to this course.