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Andrew Ng, Younes Bensouda Mourri, and Kian Katanforoosh

Из этого курса вы узнаете, как создавать успешные проекты по машинному обучению. Вы — лидер команды по внедрению ИИ или хотите им стать? Этот курс научит вас ставить правильные цели для своей команды.

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Из этого курса вы узнаете, как создавать успешные проекты по машинному обучению. Вы — лидер команды по внедрению ИИ или хотите им стать? Этот курс научит вас ставить правильные цели для своей команды.

Многое из содержимого этого курса никогда не предлагалось в других образовательных проектах и наработано на основе моего опыта построения и внедрения многочисленных проектов. В настоящий курс также включены два тренажера, которые позволят вам отработать принятие решений в ходе организации проектов по машинному обучению. Тренажеры дадут вам опыт, на получение которого иначе могло бы потребоваться несколько лет работы в машинном обучении.

После двух недель занятий вы:

— поймете, как диагностировать ошибки в системах машинного обучения;

— научитесь выделять наиболее перспективные направления для снижения количества ошибок;

— получите знания о сложных настройках машинного обучения, таких как несоответствие наборов для обучения тестовым наборам, и сравнении показателей машины с показателями человеческого уровня;

— узнаете, как применять сквозное обучение (end-to-end learning), перенос обучения (transfer learning) и многозадачное обучение (multi-task learning).

Я видел, как команды специалистов впустую тратили месяцы и даже годы работы потому, что не понимали принципы, излагаемые в этом курсе. Я надеюсь, что этот двухнедельный курс сэкономит месяцы вашего времени.

Он независим от других, и для его прохождения нужны только базовые знания в области машинного обучения. Это третий курс специализации «Глубокое обучение».

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What's inside

Syllabus

Стратегия в области машинного обучения (ML)
Стратегия машинного обучения (2)

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Адресовано руководителям команд внедрения ИИ и тем, кто планирует ими стать
Инструктор Эндрю Ын признан за его новаторский вклад в машинное обучение
Содержит уникальный контент на основе опыта инструктора в реализации проектов машинного обучения
Помогает диагностировать ошибки в системах машинного обучения
Предоставляет практический опыт через тренажеры, который равносилен нескольким годам работы в области машинного обучения
Требует базовых знаний в области машинного обучения

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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 Структурирование проектов по машинному обучению with these activities:
Review linear algebra
Linear algebra is essential for understanding the underlying mathematical concepts in machine learning.
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  • Review the basic concepts of linear algebra, such as vectors, matrices, and linear transformations.
  • Practice solving systems of linear equations.
  • Find the eigenvalues and eigenvectors of a matrix.
Review probability and statistics
Probability and statistics are essential for understanding the uncertainty and randomness inherent in machine learning.
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  • Review the basic concepts of probability, such as probability distributions, conditional probability, and Bayes' theorem.
  • Practice solving problems involving probability and statistics.
Solve machine learning practice problems
Solving practice problems will help you apply the concepts you learn in the course to real-world scenarios.
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  • Find a set of practice problems online or in a textbook.
  • Solve the problems using the techniques you learn in the course.
  • Check your solutions against the provided answer key.
Six other activities
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Follow online tutorials on machine learning
Following online tutorials will help you learn the practical aspects of machine learning.
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  • Find a set of online tutorials that cover the topics you are interested in.
  • Follow the tutorials step-by-step.
  • Complete the exercises and quizzes that are included in the tutorials.
Write a blog post or article on a machine learning topic
Writing a blog post or article will help you solidify your understanding of machine learning concepts.
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  • Choose a topic that you are interested in and that you think would be valuable for others to learn about.
  • Research the topic thoroughly.
  • Write a clear and concise blog post or article that explains the topic in a way that is easy to understand.
Attend a machine learning workshop
Attending a machine learning workshop will give you the opportunity to learn from experts in the field.
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  • Find a machine learning workshop that is offered in your area.
  • Register for the workshop.
  • Attend the workshop and participate in the activities.
Participate in a machine learning competition
Participating in a machine learning competition will challenge you to apply your skills to a real-world problem.
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  • Find a machine learning competition that you are interested in.
  • Download the data and familiarize yourself with the problem.
  • Develop a machine learning model to solve the problem.
  • Submit your model to the competition.
Contribute to an open-source machine learning project
Contributing to an open-source machine learning project will give you the opportunity to learn from others and make a valuable contribution to the community.
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  • Find an open-source machine learning project that you are interested in.
  • Read the project's documentation and contribute in a meaningful way.
  • Submit your contribution to the project.
Mentor another student who is learning machine learning
Mentoring another student will help you solidify your understanding of machine learning concepts and develop your communication skills.
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  • Find a student who is interested in learning machine learning.
  • Meet with the student regularly to answer questions and provide guidance.
  • Provide feedback on the student's work.

Career center

Learners who complete Структурирование проектов по машинному обучению will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers work in a highly technical role, implementing solutions to machine learning problems. This course will equip you to plan, implement, and deploy end-to-end machine learning solutions that bolster your company's profit margin.
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, test, and implement artificial intelligence (AI) solutions. They work with end users to identify AI-driven solutions to business problems. The ability to analyze a problem, identify weak points, and find the best solution will make you invaluable to any company looking to implement AI solutions.
Project Manager
Project Managers plan, execute, and close projects. They work with stakeholders to identify problems and develop solutions. This course will help you to identify problems that may arise in machine learning projects and find the right solutions. You will also learn how to use machine learning to spot problems early in the project lifecycle.
Data Scientist
Data Scientists design data collection, experiment, analysis and draw conclusions from it using a variety of programming tools. As someone who will be getting involved in the technical details of many corporate initiatives, this course will help you identify weak points in a project and help your team find the best approach to eliminate problems. The process of learning to identify and eliminate error is a universal skill in the field of data science.
Cloud Computing Architect
Cloud Computing Architects design, build, and maintain cloud computing systems. They work with stakeholders to identify problems and develop solutions. This course can help you build a foundation for understanding the strengths and weakness of various approaches to machine learning in the cloud. As an Architect, you can use this understanding to make informed decisions about which approaches are best for your organization.
Software Developer
Developers follow best practices to create high quality software. This course will help you stay on the cutting edge of development in the field of machine learning. The specific content on skeltonizing a project and end-to-end learning will be particularly beneficial for those who are looking to lead software development projects that involve machine learning.
Technology Consultant
Technology Consultants are responsible for understanding the strengths and weakness of various technology products and services in order to advise clients on how best to achieve their business objectives. With so many AI vendors out there, it is important to have a deep understanding of the strengths and weakness of various approaches to machine learning in order to provide proper guidance.
Solutions Architect
Solutions Architects design, build, and maintain software solutions. They work with stakeholders to identify problems and develop solutions. This course will help you understand the strengths and weaknesses of various approaches to machine learning. As a Solutions Architect, this knowledge will assist you as you design custom solutions for your clients.
Quantitative Risk Analyst
Quantitative Risk Analysts develop mathematical models to assess risk for investment banks, hedge funds, and other financial institutions. This course will help you to understand the principles that cause problems in machine learning projects. By understanding what causes problems, you can help your team avoid common pitfalls.
Systems Engineer
Systems Engineers design, build, and maintain computer systems. They work with stakeholders to identify problems and develop solutions. This course will help you understand the strengths and weaknesses of various approaches to machine learning. As a Systems Engineer, this knowledge will assist you as you design custom solutions for your clients.
Quantitative Analyst
Quantitative Analysts develop and implement strategies for identifying and assessing financial risk. As someone who analyzes large datasets, you must be able to diagnose error in statistical models. This course will help you to identify areas where your models may be inaccurate and how to adjust them to increase their effectiveness.
Data Architect
Data Architects design, build, and maintain data management systems so that data can be used effectively in the organization. As a leader in your organization, you should have some understanding of the weaknesses and pitfalls when working with machine learning projects. This course will give you an edge in organizing your teams around successful machine learning projects.
Data Analyst
Data Analysts collect, clean, analyze, and present data. They work with stakeholders to identify problems and develop solutions. This course will teach you how to detect and analyze error in machine learning models. The ability to find the cause of problems will speed up your analysis and make your findings more reliable.
Product Manager
Product Managers collaborate with engineering, design, and marketing teams to develop and launch products. In the role of product manager, you will work with teams to devise new features for your company's products. This course will give you the skills you need to gather feedback from your team and users and improve your company's machine learning products in response.
Business Analyst
Business Analysts analyze an organization's business processes and make recommendations for improvement. They work with stakeholders to identify problems and develop solutions. This course will help you to understand the principles that cause problems in machine learning projects. By understanding what causes problems, you can help your team avoid common pitfalls.

Reading list

We've selected ten 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 Структурирование проектов по машинному обучению.
Provides a probabilistic foundation for machine learning, which is useful for understanding the theoretical underpinnings of the field.
Книга, которая научит выполнять задачи по анализу данных в Python, используя библиотеки Pandas, NumPy и Matplotlib, что полезно для понимания подготовки и обработки данных, необходимых для машинного обучения.

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