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Машинное обучение с использованием Python

SAEED AGHABOZORGI and Joseph Santarcangelo
Этот курс посвящен основам машинного обучения с использованием распространенного языка программирования — Python. \Курс включает два основных раздела. Во-первых, вы узнаете о целях и задачах машинного обучения и способах применения этой технологии для решения...
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Этот курс посвящен основам машинного обучения с использованием распространенного языка программирования — Python. \Курс включает два основных раздела. Во-первых, вы узнаете о целях и задачах машинного обучения и способах применения этой технологии для решения реальных задач. Во-вторых, вы получите общее представление об отличиях между свободным и контролируемым машинным обучением, оценке моделей и алгоритмах машинного обучения. В рамках этого курса вы рассмотрите возможности применения технологий машинного обучения на практике и убедитесь в том, что они влияют на окружающий мир больше, чем вы могли себе представить! Для этого вам понадобится посвятить курсу всего несколько часов в неделю на протяжении нескольких недель. 1) Новые навыки для резюме: регрессия, систематизация, кластеризация, Scikit-learn и SciPy 2) Новые проекты для вашего портфолио, в том числе в сферах диагностики рака, прогнозирования экономических тенденций, прогнозирования оттока клиентов, рекомендательных систем и многое другое. 3) Сертификат о прохождении курса «Машинное обучение», подтверждающий вашу квалификацию который можно предъявлять физически и виртуально, например добавить к профилю в LinkedIn и других социальных сетях. Если вы пройдете этот курс и получите сертификат Coursera, то также получите цифровой значок IBM.
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Taught by professors highly recognized in the machine learning community and with active roles in industry
Offers hands-on practice with real-time applications of machine learning in various sectors
Walk learners through core concepts and methods, such as regression, clustering and classification
Emphasizes machine learning for beginners who lack a technical background in the subject
Provides a comprehensive introduction to supervised and unsupervised learning models
Introduces the Scikit-learn library for implementing machine learning algorithms in Python, which is commonly used in industry

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Career center

Learners who complete Машинное обучение с использованием Python will develop knowledge and skills that may be useful to these careers:
Data Scientist
The course, Machine Learning with Python, provides a solid foundation for individuals seeking to become Data Scientists. With its focus on the Python programming language, widely used in the field, and its coverage of foundational concepts like supervised and unsupervised learning, this course empowers learners to develop and deploy data-driven solutions. Whether aspiring to enter the field or advance in their current role, this course offers valuable insights into the practical applications of machine learning, enhancing their credibility and skillset.
Machine Learning Engineer
The course, Machine Learning with Python, is an excellent starting point for aspiring Machine Learning Engineers. It provides a strong foundation in the fundamentals of machine learning, including supervised and unsupervised learning algorithms. Through hands-on projects, learners gain practical experience in building and deploying machine learning models using the Python programming language, a popular tool in the industry. This course prepares individuals to contribute effectively to the development and implementation of machine learning solutions.
Data Analyst
For individuals interested in a career as a Data Analyst, the course, Machine Learning with Python, provides valuable knowledge and skills. Its focus on data analysis techniques, such as regression and classification, equips learners with the ability to extract meaningful insights from data. Moreover, the course's emphasis on Python, a widely adopted language in data analysis, enhances their employability in this competitive field.
Software Engineer
The course, Machine Learning with Python, can be a beneficial asset for Software Engineers seeking to expand their skillset and explore the field of machine learning. It introduces the fundamental concepts of machine learning and provides hands-on experience in developing and deploying machine learning models using Python. This knowledge enhances their ability to create innovative software solutions that leverage data and machine learning techniques.
Business Analyst
Individuals aspiring to become Business Analysts can benefit from the course, Machine Learning with Python. It provides an understanding of how machine learning can be applied to solve business problems, such as customer segmentation and demand forecasting. By gaining proficiency in Python, learners can leverage their analytical skills to develop data-driven insights and make informed business decisions, enhancing their value as Business Analysts.
Product Manager
The course, Machine Learning with Python, may be helpful for Product Managers who want to enhance their understanding of how machine learning can drive product innovation and improve user experiences. It provides an overview of machine learning concepts and their practical applications, enabling Product Managers to make informed decisions about incorporating machine learning into their products and roadmaps.
Quantitative Analyst
The course, Machine Learning with Python, may be useful for Quantitative Analysts seeking to develop a foundation in machine learning. It provides an introduction to supervised and unsupervised learning techniques and their applications in finance. By gaining proficiency in Python, learners can enhance their ability to analyze and model financial data, contributing to more accurate predictions and informed decision-making.
Research Analyst
The course, Machine Learning with Python, may be useful for Research Analysts seeking to enhance their data analysis capabilities and explore the field of machine learning. It provides an understanding of machine learning algorithms and their applications in data analysis. By gaining proficiency in Python, learners can strengthen their ability to conduct research, analyze data, and derive meaningful insights.
Data Engineer
The course, Machine Learning with Python, may be useful for Data Engineers who want to expand their knowledge of machine learning and enhance their data engineering skills. It provides an overview of machine learning concepts and their practical applications in data engineering. By gaining proficiency in Python, learners can strengthen their ability to design and implement data pipelines and leverage machine learning techniques to improve data quality and efficiency.
Statistician
The course, Machine Learning with Python, may be useful for Statisticians who want to explore the intersection of statistics and machine learning. It provides an introduction to machine learning algorithms and their applications in statistical analysis. By gaining proficiency in Python, learners can enhance their ability to analyze and interpret data using both traditional statistical methods and machine learning techniques.
Consultant
The course, Machine Learning with Python, may be useful for Consultants who work with clients seeking to leverage machine learning to solve business problems. It provides an overview of machine learning concepts and their practical applications in various industries. By gaining proficiency in Python, learners can enhance their ability to advise clients on the implementation of machine learning solutions and communicate technical concepts effectively.
Financial Analyst
The course, Machine Learning with Python, may be useful for Financial Analysts seeking to explore the applications of machine learning in finance. It provides an introduction to machine learning algorithms and their use in financial modeling and analysis. By gaining proficiency in Python, learners can enhance their ability to develop and implement machine learning models for financial forecasting, risk assessment, and portfolio optimization.
Actuary
The course, Machine Learning with Python, may be useful for Actuaries who want to explore the applications of machine learning in insurance and risk management. It provides an introduction to machine learning algorithms and their use in actuarial modeling and analysis. By gaining proficiency in Python, learners can enhance their ability to develop and implement machine learning models for insurance pricing, risk assessment, and financial planning.
Biostatistician
The course, Machine Learning with Python, may be useful for Biostatisticians who want to explore the applications of machine learning in healthcare and medical research. It provides an introduction to machine learning algorithms and their use in medical data analysis and modeling. By gaining proficiency in Python, learners can enhance their ability to develop and implement machine learning models for disease diagnosis, treatment optimization, and drug discovery.
Operations Research Analyst
The course, Machine Learning with Python, may be useful for Operations Research Analysts who want to explore the applications of machine learning in operations management and optimization. It provides an introduction to machine learning algorithms and their use in decision-making, resource allocation, and supply chain management. By gaining proficiency in Python, learners can enhance their ability to develop and implement machine learning models for improving operational efficiency and maximizing business outcomes.

Reading list

We've selected 11 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 с использованием Python.
Comprehensive guide to deep learning, covering advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for researchers and practitioners who want to stay up-to-date with the latest advancements in deep learning.
Focuses on practical machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It provides step-by-step instructions and examples for building and deploying machine learning models.
Presents machine learning from a probabilistic perspective. It valuable resource for researchers and practitioners who want to understand the theoretical foundations of machine learning.
Provides a comprehensive overview of machine learning with TensorFlow. It valuable resource for both beginners and experienced practitioners who want to use TensorFlow for machine learning.
Provides a comprehensive overview of machine learning algorithms. It valuable resource for both beginners and experienced practitioners who want to deepen their understanding of machine learning methods.
Provides a practical introduction to machine learning with Python. It is written in a hands-on style, with plenty of code examples.
Provides a practical introduction to machine learning for software developers. It is written in a hands-on style, with plenty of code examples.
Provides a gentle introduction to machine learning for beginners. It is written in a clear and concise style, making it easy to understand even for those with no prior knowledge of machine learning.

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