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Машинное обучение в финансах

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...
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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

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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
A solid understanding of Python is essential for implementing ML algorithms and working with financial data.
Browse courses on Python Programming
Show steps
  • 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
Reviewing Logistic Regression helps build a strong understanding of fundamental modeling concepts used subsequent sections of the ML in finance course.
Show steps
  • 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
This activity strengthens Python programming skills and prepares you for implementing ML algorithms in this course.
Browse courses on Python Programming
Show steps
  • 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
Expand to see all activities and additional details
Show all five activities
Join a study group or participate in online forums related to ML in finance
Engaging with peers and experts helps reinforce concepts, clarify doubts, and gain diverse perspectives on ML in finance.
Show steps
  • 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
This book showcases real-world examples of how ML is transforming the finance industry, providing insights into emerging trends and best practices.
Show steps
  • 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

Reading list

We've selected 17 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 Машинное обучение в финансах.
Is written in Russian and provides a comprehensive introduction to machine learning in finance. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a practical introduction to machine learning techniques used in the financial industry. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a comprehensive introduction to machine learning using Python. It covers a wide range of topics, including data preprocessing, model selection, and hyperparameter tuning.
Practical guide to machine learning for asset managers. It covers a wide range of topics, including data preprocessing, model selection, and risk management.
Provides a practical introduction to deep learning using fastai and PyTorch. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and transformers.
Provides a comprehensive overview of artificial intelligence in finance. It covers the latest research and applications in the field.
Practical guide to Python for finance. It covers the basics of Python, as well as how to use Python for financial data analysis and modeling.
Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, including linear regression, logistic regression, and support vector machines.
Is written in Russian and provides a comprehensive introduction to artificial intelligence and machine learning. It covers a wide range of topics, including the history of AI, the different types of AI algorithms, and the applications of AI.
Presents a step-by-step approach to machine learning for predictive analytics in finance. It covers a wide range of topics, including data preprocessing, model selection, and risk management.
Covers the latest machine learning techniques for financial risk management. It valuable resource for anyone looking to learn more about machine learning in finance.
Comprehensive guide to machine learning for financial engineering. It covers a wide range of topics, including data preprocessing, model selection, and risk management.
Provides a gentle introduction to machine learning using Python. It covers a wide range of topics, including data preprocessing, model selection, and hyperparameter tuning.
Covers the latest machine learning techniques for financial engineering. It valuable resource for anyone looking to learn more about machine learning in finance.
Comprehensive guide to machine learning for financial risk management. It valuable resource for anyone looking to learn more about machine learning in finance.
Provides a comprehensive introduction to econometrics. It covers a wide range of topics, including linear regression, time series analysis, and panel data.

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