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Janani Ravi
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.
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.
Read more
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.
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.
Read more
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.
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/6psi1z/machine
What's inside
Syllabus
Course Overview
Exploring Applications of Machine Learning in Financial Services
Case Study: Quantifying Risk and Return of Investment Opportunities
Read more
Syllabus
Course Overview
Exploring Applications of Machine Learning in Financial Services
Case Study: Quantifying Risk and Return of Investment Opportunities
Read more
Case Study: Extracting Insights for Fraud Detection
Applying Machine Learning Techniques to Financial Data
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Geared toward existing analysts with a background in finance, this course assumes familiarity with analytical and statistical models
Led by industry practitioners Janani Ravi
Builds awareness of machine learning's applications in the financial services sector
Incorporates case studies that demonstrate real-world usage
Includes hands-on exercises to reinforce concepts
Save this course
Save Machine Learning for Financial Services to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Machine Learning for Financial Services with these
activities:
Review Basic Data Analysis
Show steps
Refresh foundational skills in data analysis to prepare for advanced topics in machine learning techniques in financial services.
Browse courses on
Descriptive Statistics
Show steps
-
Revise statistical concepts like mean, median, mode and standard deviation.
-
Review methods of data visualization like histograms, scatterplots and box plots.
Review financial services modeling basics
Show steps
Review the basics of financial services modeling to strengthen foundational knowledge and enhance understanding of the course material.
Browse courses on
Financial Modeling
Show steps
-
Read introductory articles or textbooks on financial services modeling.
-
Review notes or summaries of past courses or self-study materials on financial modeling.
Join a study group for peer support
Show steps
Connect with fellow students to form a study group for peer support, knowledge sharing, and collaborative problem-solving to enhance learning and retention.
Show steps
-
Reach out to classmates or online forums to find interested individuals for a study group.
-
Establish regular meeting times and a platform for communication.
-
Collaborate on assignments, share resources, and engage in discussions to strengthen understanding.
Show all three activities
Review Basic Data Analysis
Show steps
Refresh foundational skills in data analysis to prepare for advanced topics in machine learning techniques in financial services.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Revise statistical concepts like mean, median, mode and standard deviation.
- Review methods of data visualization like histograms, scatterplots and box plots.
Review financial services modeling basics
Show steps
Review the basics of financial services modeling to strengthen foundational knowledge and enhance understanding of the course material.
Browse courses on
Financial Modeling
Show steps
Show steps
Show steps
- Read introductory articles or textbooks on financial services modeling.
- Review notes or summaries of past courses or self-study materials on financial modeling.
Join a study group for peer support
Show steps
Connect with fellow students to form a study group for peer support, knowledge sharing, and collaborative problem-solving to enhance learning and retention.
Show steps
Show steps
Show steps
- Reach out to classmates or online forums to find interested individuals for a study group.
- Establish regular meeting times and a platform for communication.
- Collaborate on assignments, share resources, and engage in discussions to strengthen understanding.
Show all three activities
Career center
Learners who complete Machine Learning for Financial Services will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist combines programming knowledge with a deep understanding of statistics and machine learning. They study data using advanced analytic techniques. Many Data Scientists work in the financial industry where they use machine learning to make predictions about investments, automate processes, and detect fraud. This course will teach you how to do all of these things and can help you land a job as a Data Scientist in financial services.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst (Quant) develops and implements mathematical models to solve problems in the financial industry. They may work on projects such as pricing financial instruments, developing trading strategies, or managing risk. Machine learning is a core skill for Quants and this course may help you qualify for roles in the financial services industry.
Financial Analyst
Financial Analyst
A Financial Analyst uses their understanding of finance and economics to advise individuals and organizations on investment decisions. Their work involves forecasting trends in the economy as well as analyzing data to assess the performance of companies. Machine learning is increasingly used by Financial Analysts to help with this analysis. This course will help you develop the skills you need to become a successful Financial Analyst in the financial services industry.
Risk Analyst
Risk Analyst
A Risk Analyst assesses and manages financial risk for companies. They use various techniques to identify and measure risk, and then develop strategies to mitigate those risks. Machine learning is increasingly used by Risk Analysts to automate and improve this process. This course can help you build the skills you need to become a successful Risk Analyst in the financial services industry.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring a product to market. Financial services companies are increasingly using machine learning to develop new products and services. This course can help you build the skills you need to become a Product Manager in the financial services industry.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. They work on a variety of projects, including developing new models, improving existing models, and integrating models into production systems. Financial services companies are increasingly using machine learning to develop new products and services. This course can help you build the skills you need to become a Machine Learning Engineer in the financial services industry.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They work on a variety of projects, including developing new features, fixing bugs, and improving performance. Financial services companies are increasingly using machine learning to develop new products and services. This course can help you build the skills you need to become a Software Engineer in the financial services industry.
Data Analyst
Data Analyst
A Data Analyst collects, cleans, and analyzes data to help businesses make informed decisions. They work on a variety of projects, including developing reports, creating visualizations, and identifying trends. Financial services companies are increasingly using machine learning to automate and improve this process. This course can help you build the skills you need to become a Data Analyst in the financial services industry.
Investment Analyst
Investment Analyst
An Investment Analyst researches and analyzes investment opportunities for individuals and organizations. They make recommendations on which investments to buy, sell, or hold. Machine learning is increasingly used by Investment Analysts to help with this analysis. This course will help you develop the skills you need to become a successful Investment Analyst in the financial services industry.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and systems to identify areas for improvement. They work with stakeholders to develop and implement solutions that improve efficiency and effectiveness. Financial services companies are increasingly using machine learning to automate and improve their business processes. This course can help you build the skills you need to become a successful Business Analyst in the financial services industry.
Regulatory Affairs Specialist
Regulatory Affairs Specialist
A Regulatory Affairs Specialist ensures that a company complies with all applicable regulations. They work with a variety of departments to develop and implement regulatory compliance policies and procedures. Machine learning is increasingly used by Regulatory Affairs Specialists to automate and improve this process. This course may help you build the skills you need to become a successful Regulatory Affairs Specialist in the financial services industry.
Financial Advisor
Financial Advisor
A Financial Advisor provides financial advice to individuals and organizations. They help clients with a variety of financial planning needs, such as retirement planning, investment management, and estate planning. Machine learning is increasingly used by Financial Advisors to help with this analysis. This course may help you develop the skills you need to become a successful Financial Advisor in the financial services industry.
Fraud Investigator
Fraud Investigator
A Fraud Investigator investigates cases of fraud and embezzlement. They work with law enforcement to identify and apprehend fraudsters. Machine learning is increasingly used by Fraud Investigators to automate and improve this process. This course may help you build the skills you need to become a successful Fraud Investigator in the financial services industry.
Compliance Officer
Compliance Officer
A Compliance Officer ensures that a company complies with all applicable laws and regulations. They work with a variety of departments to develop and implement compliance policies and procedures. Machine learning is increasingly used by Compliance Officers to automate and improve this process. This course may help you build the skills you need to become a successful Compliance Officer in the financial services industry.
Actuary
Actuary
An Actuary uses mathematical and statistical techniques to assess risk and uncertainty. They work on a variety of projects, including pricing insurance policies, developing pension plans, and managing financial risk. Machine learning is increasingly used by Actuaries to automate and improve this process. This course may help you build the skills you need to become a successful Actuary in the financial services industry.
For more career information including salaries, visit:
OpenCourser.com/course/6psi1z/machine
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning for Financial Services .
This textbook is written by the instructor of the course. It covers the conceptual aspects of applying machine learning to problems in the financial services industry and discusses case studies of machine learning used in financial services.
Practical guide to machine learning for finance. It covers the basics of machine learning, as well as more advanced topics such as time series analysis and natural language processing. It also provides case studies of how machine learning is being used in the financial services industry.
Provides a comprehensive overview of machine learning techniques and their applications in the financial industry. It covers topics such as supervised learning, unsupervised learning, and time series analysis.
Comprehensive introduction to artificial intelligence in finance. It covers the basics of artificial intelligence, as well as more advanced topics such as machine learning, deep learning, and natural language processing. It also provides case studies of how artificial intelligence is being used in the financial services industry.
Provides a comprehensive overview of artificial intelligence techniques and their applications in the financial industry. It covers topics such as natural language processing, computer vision, and machine learning.
Practical guide to machine learning for finance. It covers the basics of machine learning, as well as more advanced topics such as time series analysis and natural language processing. It also provides case studies of how machine learning is being used in the financial services industry.
Practical guide to machine learning for asset management. It covers the basics of machine learning, as well as more advanced topics such as time series analysis and natural language processing. It also provides case studies of how machine learning is being used in the financial services industry.
Practical guide to machine learning for real estate. It covers the basics of machine learning, as well as more advanced topics such as time series analysis and natural language processing. It also provides case studies of how machine learning is being used in the financial services industry.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6psi1z/machine
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Time
111 hours
Level
Introductory
Via
Pluralsight
Instructor
Janani Ravi
Language
English
Good to know
Geared toward existing analysts with a background in finance, this course assumes familiarity with analytical and statistical models
Led by industry practitioners Janani Ravi
Builds awareness of machine learning's applications in the financial services sector
Incorporates case studies that demonstrate real-world usage
Includes hands-on exercises to reinforce concepts
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