Structurer des projets d’apprentissage automatique from Coursera

Vous allez apprendre à mener à bien un projet d’apprentissage automatique. Si vous souhaitez devenir un leader technique en IA et que vous savez comment orienter le travail de votre équipe, ce cours va vous montrer la marche à suivre.

Une grande partie de ce contenu n’a jamais été enseignée ailleurs ; elle est tirée de mon expérience dans la construction et l’expédition de nombreux produits d’apprentissage profond. Cette formation comprend également deux "simulateurs de vol" qui vous permettront de pratiquer la prise de décision comme chef de projet d’apprentissage automatique. Cela vous fournira une "expérience industrielle" que vous ne pourriez seulement obtenir d’une autre manière qu’après des années d’expérience de travail d'apprentissage automatique (AA).

Après 2 semaines, vous:

- Comprendrez comment diagnostiquer les erreurs d’un système d’apprentissage automatique, et

- Pourrez choisir les priorités sur les orientations les plus prometteuses dans la réduction d’erreurs

- Comprendrez les paramètres AA complexes, tels que les ensembles ou jeux d'entraînement/tests inadéquats, et comparer et/ou surpasser les performances humaines

- Saurez appliquer l’apprentissage, l’apprentissage de transfert et l’apprentissage multitâche de bout en bout

J'ai vu des équipes perdre des mois, voire des années, à ne pas comprendre les principes qui sont enseignés dans ce cours. J’espère que cette formation de deux semaines vous fera donc gagner des mois.

C’est un cours autonome ; vous pouvez le suivre aussi longtemps que vous avez des connaissances de base en apprentissage automatique. C’est le troisième cours de spécialisation en apprentissage en profondeur.

What's inside

Syllabus

Stratégie AA (1)

Stratégie AA (2)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Développe les compétences en apprentissage automatique (AA), un domaine très recherché dans l'industrie

Enseigné par des experts reconnus dans le domaine de l'AA, Andrew Ng et Younes Bensouda Mourri

Offre des simulations pratiques pour appliquer les concepts AA dans des scénarios réels

Requiert des connaissances de base en AA, ce qui peut être un obstacle pour les débutants

Reviews summary

Learn ml projects framework

This course will teach you how to lead a successful machine learning project. Students in the course highly recommend it for its clear and didactic approach to machine learning project management. The course includes two "flight simulators" to help you practice decision-making as a machine learning project leader, providing you with industry experience that would otherwise take years to gain. Overall, this course is highly recommended for those who want to become technical leaders in AI and learn how to guide their team's work.

The course is clear and easy to understand.

"Very clear and didactic! Really very helpful and many great advices !"

Course includes "flight simulators" for practice.

"The course includes two "flight simulators" that will allow you to practice decision-making as a machine learning project leader."

Career center

Learners who complete Structurer des projets d’apprentissage automatique will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers are problem-solvers who design, develop, implement, and maintain machine learning solutions. The field of machine learning has exploded in the past decade, and Machine Learning Engineers are in high demand at major companies in virtually every major industry. Becoming a Machine Learning Engineer requires a strong foundation in computer science, statistics, and mathematics. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Data Scientist

Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. They work with large datasets to identify patterns and trends, and they develop algorithms to predict future outcomes. Becoming a Data Scientist requires a strong foundation in computer science, statistics, and mathematics. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They work with a variety of programming languages and technologies to create software that meets the needs of users. Becoming a Software Engineer requires a strong foundation in computer science and mathematics. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They develop trading strategies and make investment recommendations. Becoming a Quantitative Analyst requires a strong foundation in mathematics, statistics, and finance. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. They work with a variety of industries to improve efficiency and productivity. Becoming an Operations Research Analyst requires a strong foundation in mathematics, statistics, and computer science. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Business Analyst

Business Analysts use data and analytics to solve business problems. They work with a variety of industries to improve efficiency and productivity. Becoming a Business Analyst requires a strong foundation in business, mathematics, and statistics. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Data Analyst

Data Analysts use data to solve business problems. They work with a variety of industries to improve efficiency and productivity. Becoming a Data Analyst requires a strong foundation in mathematics, statistics, and computer science. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Statistician

Statisticians use mathematical and statistical models to analyze data. They work with a variety of industries to improve efficiency and productivity. Becoming a Statistician requires a strong foundation in mathematics and statistics. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Financial Analyst

Financial Analysts use financial data to make investment recommendations. They work with a variety of industries to help businesses make informed decisions. Becoming a Financial Analyst requires a strong foundation in finance and mathematics. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Market Research Analyst

Market Research Analysts use data to understand consumer behavior. They work with a variety of industries to help businesses make informed decisions. Becoming a Market Research Analyst requires a strong foundation in business and statistics. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Biostatistician

Biostatisticians use mathematical and statistical models to analyze data in the medical field. They work with a variety of healthcare professionals to improve patient outcomes. Becoming a Biostatistician requires a strong foundation in mathematics, statistics, and biology. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Epidemiologist

Epidemiologists use data to investigate the causes of disease. They work with a variety of healthcare professionals to improve public health. Becoming an Epidemiologist requires a strong foundation in mathematics, statistics, and biology. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Actuary

Actuaries use mathematical and statistical models to assess risk. They work with a variety of industries to help businesses make informed decisions. Becoming an Actuary requires a strong foundation in mathematics and statistics. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Economist

Economists use data to analyze economic trends. They work with a variety of industries to help businesses make informed decisions. Becoming an Economist requires a strong foundation in mathematics and economics. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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Consultant

Consultants use their knowledge and expertise to help businesses solve problems. They work with a variety of industries to help businesses improve efficiency and productivity. Becoming a Consultant requires a strong foundation in business and a variety of other fields. This course will provide you with a solid understanding of the fundamentals of machine learning, as well as the skills you need to apply machine learning to real-world problems.

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