Analytics
Exploratory Data Analysis
Machine Learning for Financial Services
This course will explore the conceptual aspects of applying machine learning to problems in the financial services industry and discuss case studies of machine learning used in financial services.
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₹50,004
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₹2,370/mo
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Course Insight
Suitable for beginner learners. This course serves as an entry point into Data Science, building foundational knowledge before moving on to advanced frameworks or specialized paths.
Beginner FriendlySelf-Paced LearningHands-On Learning
SKILLS TO
MASTER
ModelingTrending
Predictive Machine LearningSQL Querying
Relational Data ManagementCareers:Relevant for professionals pursuing roles within Data Science.
💡This course fits perfectly into our comprehensiveData Science Learning Path. Explore the ecosystem to see how it compares to other foundational skills.
Quick Facts
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What You’ll Learn
- Course Overview : 1min.
- Exploring Applications of Machine Learning in Financial Services : 38mins.
- Case Study: Quantifying Risk and Return of Investment Opportunities : 24mins.
- Case Study: Extracting Insights for Fraud Detection : 18mins.
- Applying Machine Learning Techniques to Financial Data : 27mins.
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Description
Analytical and statistical models are already an integral part of the finance industry and the use of machine learning builds on a strong foundation in this industry. The financial services industry is uniquely positioned to leverage machine learning because of the vast quantities of high-quality data already available.
In this course, Machine Learning for Financial Services, you will explore machine learning techniques currently applied in the financial services industry. First, you will look at some examples and cases of where ML is already being used in financial services - for investment predictions, loan automation, process automation, and fraud detection. Then, you will develop an intuitive understanding of how recurrent neural networks
Next, you will explore two ML case studies from research papers - the first focusing on assessing and quantifying the return on investment and the second exploring how classification and clustering models can help detect money laundering.
Finally, you will get hands-on coding and see how you can use a classification model for fraud detection on a synthetically generated dataset.
When you are finished with this course, you will have the awareness of how machine learning can be applied in the financial services industry and hands-on experience working with financial data.
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