هيكلة مشاريع التعلم الآلي from Coursera

ستتعلم كيفية بناء مشروع تعلم آلي ناجح. إذا كنت تطمح في أن تكون قائدًا تقنيًا في مجال الذكاء الاصطناعي، وتعرف كيفية تحديد اتجاه عمل فريقك فسيوضح لك هذا المساق كيفية القيام بذلك.

لم يتم تدريس الكثير من هذا المحتوى في أي مكان آخر، وهو مستمد من تجربتي في بناء وتشكيل العديد من منتجات التعلم المتعمق. يحتوي هذا المساق كذلك على "محاكي طيران" مما تتيح لك ممارسة عملية صنع القرار كقائد لمشروع التعلم الآلي. مما يمنحك "خبرة صناعية" قد تحصل عليها بخلاف ذلك بعد سنوات من الخبرة العملية.

فبعد أسبوعين ستتمكن من:

- فهم كيفية تشخيص الأخطاء في نظام التعلم الآلي،

فبعد أسبوعين ستتمكن من:

- فهم كيفية تشخيص الأخطاء في نظام التعلم الآلي،

- ستصبح قادرًا على إعطاء الأولوية للاتجاهات الواعدة لتقليل الخطأ،

- فهم إعدادات التعليم الآلي المعقدة مثل مجموعات التدريب/الاختبار غير المتطابقة ومقارنة و/أو تجاوز الأداء على مستوى البشري،

- معرفة كيفية تطبيق التعلم الشامل والنقل التعلم متعدد المهام

فقد شاهدت فرقًا تضيع شهورًا أو سنوات بسبب عدم فهم المبادئ التي يتم تدريسها في هذا المساق. وأتمنى ان يوفر عليك هذا المساق العديد من الأشهر.

فهذا مساق قائم بذاته، يمكنك حضوره طالما ان لديك أساسيات معرفة التعلم الآلي. هذا هو المساق الثالث في تخصص التعلم المتعمق.

What's inside

Syllabus

استراتيجية التعلم الآلي (1)

استراتيجية التعلم الآلي (2)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Designed for those aspiring to be tech leads in the field of AI, this course teaches how to identify actionable directions for your team

Teaches learners how to diagnose errors in machine learning systems so they can prioritize promising directions to reduce errors

Provides real-world experience in the making of data science decisions

Taught by experts in the field of deep learning

Assumes prior knowledge of machine learning

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 هيكلة مشاريع التعلم الآلي with these activities:

Review linear algebra

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Improve understanding of linear algebra concepts, which are essential for understanding deep learning algorithms.

Browse courses on Linear Algebra

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Review lecture notes and textbooks on linear algebra.
Solve practice problems and exercises.
Attend a workshop or online course on linear algebra.

Join a study group or discussion forum

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Engage in discussions and share knowledge with peers, fostering a better understanding of deep learning concepts.

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Identify or create a study group or join a discussion forum.
Participate in regular discussions and ask questions.
Share insights and collaborate on projects.

Solve coding challenges on LeetCode

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Enhance problem-solving and coding skills, which are crucial for implementing deep learning models.

Browse courses on Coding Challenges

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Register on LeetCode.
Start solving coding challenges of varying difficulty levels.
Review solutions and discuss with peers.

Four other activities

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Follow tutorials on deep learning frameworks

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Gain practical experience using deep learning frameworks, which are essential for building and deploying deep learning models.

Browse courses on TensorFlow

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Identify a deep learning framework (e.g., TensorFlow, PyTorch).
Follow online tutorials and documentation on the framework.
Build small deep learning projects using the framework.

Mentor junior students or peers in deep learning

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Solidify deep learning knowledge by teaching and guiding others, identifying and addressing gaps in understanding.

Browse courses on Mentoring

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Identify opportunities to mentor others.
Prepare tailored learning materials and resources.
Provide guidance and support to mentees.
Reflect on the mentoring experience and identify areas for improvement.

Write a blog post or article on a deep learning topic

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Reinforce understanding of deep learning concepts by explaining them to others through writing.

Browse courses on Blogging

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Choose a specific deep learning topic.
Research and gather information on the topic.
Write a clear and engaging blog post or article.
Share the blog post or article with peers and online communities.

Contribute to an open-source deep learning project

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Gain practical experience in deep learning by contributing to real-world projects, enhancing technical skills and project portfolio.

Browse courses on Open Source

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Identify an open-source deep learning project.
Review the project's documentation and codebase.
Identify areas where you can contribute.
Submit a pull request with your contribution.

Career center

Learners who complete هيكلة مشاريع التعلم الآلي will develop knowledge and skills that may be useful to these careers:

Research Scientist

Research Scientists conduct research in a variety of fields, including machine learning. This course will help you to develop the skills and knowledge necessary to conduct research in machine learning.

See salaries and explore the career path for Research Scientist

Machine Learning Researcher

Machine Learning Researchers conduct research in the field of machine learning. This course will help you to develop the skills and knowledge necessary to conduct research in machine learning.

See salaries and explore the career path for Machine Learning Researcher

Machine Learning Engineer

Machine Learning Engineers draw on theoretical knowledge of machine learning to develop solutions for a range of industries. This course would help you to gain a deeper understanding of the challenges in implementing machine learning, and this can help you make better decisions about modeling strategy.

See salaries and explore the career path for Machine Learning Engineer

Machine Learning Architect

Machine Learning Architects design and implement machine learning models and solutions that meet organizational requirements. This course will help you to develop a deeper understanding of the challenges in implementing machine learning, and this can help you to make better decisions about modeling strategy in your role.

See salaries and explore the career path for Machine Learning Architect

Artificial Intelligence Engineer

Artificial Intelligence Engineers design and develop artificial intelligence systems. This course will help you to develop the skills and knowledge necessary to design and develop machine learning solutions.

See salaries and explore the career path for Artificial Intelligence Engineer

Data Scientist

Data Scientists synthesize large amounts of structured and unstructured data to explore trends and patterns. This course will help you to develop a deeper understanding of the strategies for implementing machine learning and modeling methodologies, which will help you to make more informed decisions on modeling strategy in your role.

See salaries and explore the career path for Data Scientist

Data Engineer

Data Engineers design and implement data pipelines and infrastructure that support data-driven organizations. This course will equip you with the knowledge to implement machine learning solutions, which are increasingly critical to data engineering.

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Computer Vision Engineer

Computer Vision Engineers design and develop computer vision systems. This course may be useful if you are interested in using machine learning for computer vision applications.

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Data Architect

Data Architects design and implement data management solutions that meet the needs of organizations. This course will equip you with the knowledge to design and implement machine learning solutions, which are increasingly critical to data management.

See salaries and explore the career path for Data Architect

Data Analyst

A Data Analyst uses a range of statistical and modeling techniques to make sense of raw data, which can then be used to aid in decision-making. This course would help you to improve your ability to implement and improve analytical models that incorporate machine learning.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze data and make investment decisions. This course may be useful if you are interested in using machine learning for financial analysis.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex problems. This course may be useful if you are interested in using machine learning for operations research.

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Software Engineer

Software Engineers apply engineering principles to design, develop, deploy, and maintain software systems. This course may be useful if you are interested in developing machine learning applications.

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Business Analyst

Business Analysts use data and analysis to help businesses make better decisions. This course may be useful if you are interested in using machine learning for business analysis.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers are responsible for the overall strategy, roadmap, and execution of a product. This course may be useful if you are interested in managing machine learning products.

See salaries and explore the career path for Product Manager