Машинное обучение в финансах from Coursera

Машинное обучение (Machine Learning, или ML) — это дисциплина о том, как на основе различных алгоритмов обучить компьютер распознавать, классифицировать и предсказывать объекты. Машинное обучение подарило нам эффективный поиск и персонализированный контент в интернете, а в последнее время активно используется в финансах и банковской сфере — наш курс именно об этом! Применение методов ML помогает банку более оперативно принимать решения. Сможет ли вернуть кредит конкретный клиент? Как изменится объем вкладов и кредитов в ближайшей перспективе? Как оптимизировать внутренние процессы? Эти и многие другие проблемы финансовой сферы помогают решать на практике передовые методы ML. Если вы студент и видите свое будущее в ML в финансах, но еще не до конца понимаете, чем будете заниматься; или уже работаете в банковской/IT сфере и хотите улучшить свои знания и квалификацию, а может быть, вы просто активно интересуетесь последними тенденциями применения ML — добро пожаловать на онлайн-курс «Машинное обучение в финансах» от команды финансистов Сбербанка! Наш курс практико-ориентированный: вы узнаете о внедрении и применении ML на примере трейдинга, прогнозировании операционного дохода банка, автоматизации внутренних процессов и др., а также пройдете несколько практических заданий с использованием языка программирования Python. На второй неделе курса используется вероятностный язык программирования Stan. В лекциях и домашних заданиях по прогнозированию представлены базовые примеры моделей в Stan и ссылки на более детальное ознакомление с языком. Освоив эту программу, слушатель научится применять на практике многие методы ML и получит конкурентное преимущество для трудоустройства в финансовой и IT сфере.

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Teaches ML applications in the finance industry, including topics such as trading and operational income forecasting

Taught by finance professionals from Sberbank, providing learners with industry-specific insights

Explores topics such as credit risk assessment, portfolio optimization, and fraud detection, which are highly relevant to the finance industry

Emphasizes practical skills through hands-on Python assignments, which helps learners prepare for real-world applications

Includes probabilistic programming with the Stan language, which is commonly used in financial modeling

Suitable for students and professionals interested in applying ML to financial problems

Reviews summary

Finance machine learning

The course, 'Machine Learning in Finance,' offers an in-depth exploration of the application of machine learning in financial domains. The course features a mix of lectures and practical assignments, providing a practical understanding of machine learning. Positive feedback highlights the quality of the lectures and assignments, along with the insights gained into real-world applications of machine learning in finance. However, some reviews express concerns about the difficulty level and the lack of support for beginners.

Focuses on financial applications of ML

"Machine Learning подарило нам эффективный поиск и персонализированный контент в интернете, а в последнее время активно используется в финансах и банковской сфере — наш курс именно об этом!"

"Весьма ёмкий курс с фокусом на приложение ML в финансовой сфере."

Gains practical knowledge of ML in finance

"Наш курс практико-ориентированный"

"вы узнаете о внедрении и применении ML на примере трейдинга, прогнозировании операционного дохода банка, автоматизации внутренних процессов и др., а также пройдете несколько практических заданий с использованием языка программирования Python."

High-quality lectures and assignments

"Качественный курс, лекции, практические задания."

Limited support for troubleshooting

"нет возможности проверить правильность выполнения задания ввиду "мёртвого" форума и отсутствия "решения преподавателей""

Can be difficult for beginners

"Без предварительной подготовки и хороших знаний языка Python будет очень сложно."

"Этот курс не для вас."

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 Машинное обучение в финансах with these activities:

Review Python programming basics

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A solid understanding of Python is essential for implementing ML algorithms and working with financial data.

Browse courses on Python Programming

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Go through online tutorials or revisit materials from previous Python courses
Practice writing simple Python scripts to manipulate data, perform basic operations, and create visualizations
Solve coding challenges or puzzles to reinforce your understanding of Python syntax and data structures

Reinforce fundamentals by reviewing Logistic Regression

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Reviewing Logistic Regression helps build a strong understanding of fundamental modeling concepts used subsequent sections of the ML in finance course.

View Applied Logistic Regression on Amazon

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Read through key chapters of the book that cover Logistic Regression including a description and comparison with linear regression, model fitting and evaluation, diagnostics and model selection
Complete end of chapter exercises to practice computations and diagnostics
If time permits, review supplementary material on Logistic Regression such as online articles and videos to enhance your understanding

Solve coding challenges using Python

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This activity strengthens Python programming skills and prepares you for implementing ML algorithms in this course.

Browse courses on Python Programming

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Join online coding platforms such as LeetCode or HackerRank
Select coding challenges tagged with Python and difficulty levels appropriate to your skillset
Solve the challenges, debugging and optimizing your code
Review solutions and explanations provided by the platform or community
Repeat the process to improve your problem-solving and coding proficiency

Two other activities

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Join a study group or participate in online forums related to ML in finance

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Engaging with peers and experts helps reinforce concepts, clarify doubts, and gain diverse perspectives on ML in finance.

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Identify and join relevant study groups or online communities focused on ML in finance
Actively participate in discussions, ask questions, and share your own insights
Collaborate with other members on projects or assignments
Attend live Q&A sessions or webinars organized by the group to interact with experts

Read about cutting-edge applications of ML in finance

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This book showcases real-world examples of how ML is transforming the finance industry, providing insights into emerging trends and best practices.

View Asset Management: Tools and Issues on Amazon

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Acquire a copy of the book and allocate dedicated time for reading
Read through the chapters that cover applications of ML in various areas of finance, such as portfolio management, risk assessment, and fraud detection
Take notes and highlight key concepts and examples
If possible, engage in discussions with peers or experts to exchange perspectives on the book's content

Career center

Learners who complete Машинное обучение в финансах will develop knowledge and skills that may be useful to these careers:

Quantitative Analyst

Quantitative Analysts develop and apply mathematical and statistical models to solve financial problems, using advanced techniques to analyze data and make predictions. The course covers topics relevant to Quantitative Analysis, such as Machine Learning algorithms for financial modeling. It may be useful for aspiring Quantitative Analysts who want to develop expertise in ML applications in finance.

See salaries and explore the career path for Quantitative Analyst

Machine Learning Engineer

Machine Learning Engineers design, develop, and implement Machine Learning models to address specific problems. This course may be useful for aspiring Machine Learning Engineers who want to specialize in the financial domain. It covers topics such as ML applications in finance, and provides practical experience in building and deploying ML models.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to extract insights and inform decision-making. This course may be useful for aspiring Data Analysts who want to specialize in the financial industry. It covers topics such as ML applications in finance and provides hands-on experience in data analysis.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems, applying engineering principles to solve technical problems. This course may be useful for aspiring Software Engineers who want to specialize in Machine Learning or financial applications. It covers topics such as software engineering practices and the application of Machine Learning in software development.

See salaries and explore the career path for Software Engineer

Risk Analyst

Risk Analysts assess and manage risks within an organization, identifying potential threats and vulnerabilities to develop mitigation strategies. This course covers topics relevant to Risk Analysis, such as Machine Learning algorithms for risk assessment. It could be useful for aspiring Risk Analysts who want to develop expertise in ML applications in risk management.

See salaries and explore the career path for Risk Analyst

Financial Risk Manager

Financial Risk Managers assess and manage financial risk within an organization, using quantitative and qualitative techniques to measure and mitigate risk exposure. This course covers topics relevant to Financial Risk Management, such as Machine Learning algorithms for predicting financial risk. It may be useful for aspiring Financial Risk Managers who want to develop expertise in ML applications in risk management.

See salaries and explore the career path for Financial Risk Manager

Investment Analyst

Investment Analysts research and analyze investment opportunities to make recommendations to clients. This course could be useful for aspiring Investment Analysts who want to learn about applications of Machine Learning in financial markets. It covers topics such as using ML to analyze market data and build investment models.

See salaries and explore the career path for Investment Analyst

Actuary

Actuaries use mathematical and statistical methods to assess and manage financial risk, applying knowledge of insurance, finance, and statistics to solve problems. This course covers topics relevant to Actuarial Science, such as Machine Learning algorithms for insurance risk assessment. It could be useful for aspiring Actuaries who want to develop expertise in ML applications in insurance.

See salaries and explore the career path for Actuary

Business Analyst

Business Analysts gather and analyze business requirements to improve processes and systems, providing insights and recommendations to stakeholders. This course could be useful for aspiring Business Analysts who want to learn about applications of Machine Learning in business analysis. It covers topics such as using ML to optimize processes and identify business opportunities.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers are responsible for the development and launch of new products or features, working closely with engineering and design teams to bring products to market. This course could be useful for aspiring Product Managers who want to learn about ML applications in product development. It covers topics such as using ML to gather customer insights and improve product functionality.

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Financial Analyst

Financial Analysts assess the performance of companies and industries by modeling, forecasting, and evaluating financial information to make informed recommendations. This course helps build a foundation for Financial Analysis by covering Machine Learning techniques used in the financial industry. It could be useful for aspiring Financial Analysts who want to learn about ML applications in finance.

See salaries and explore the career path for Financial Analyst

Data Scientist

Data Scientists apply mathematical and computational principles to solve complex problems using data, examining and analyzing data to identify patterns and trends. This course covers topics that help build a foundation for Data Science, such as Machine Learning. Enrollees can learn the basics of building and implementing ML models, and explore applications of ML in financial domains. It could be useful for aspiring Data Scientists who want to specialize in finance.

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Financial Planner

Financial Planners provide financial advice and guidance to individuals and families, helping them plan for financial security and achieve their financial goals. This course could be useful for aspiring Financial Planners who want to learn about applications of Machine Learning in financial planning. It covers topics such as using ML to analyze financial data and make personalized recommendations.

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Investment Banker

Investment Bankers provide financial advice and services to corporations and governments, helping them raise capital and manage financial transactions. This course could be useful for aspiring Investment Bankers who want to learn about applications of Machine Learning in investment banking. It covers topics such as using ML to analyze market data and make investment recommendations.

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Auditor

Auditors examine and evaluate financial records and accounting practices to ensure accuracy and compliance with regulations. This course could be useful for aspiring Auditors who want to learn about applications of Machine Learning in auditing. It covers topics such as using ML to analyze financial data and identify potential accounting errors or fraud.

See salaries and explore the career path for Auditor