Машинное обучение с использованием Python from Coursera

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.

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

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 с использованием Python with these activities:

Learn the basics of Python for Machine Learning

Show steps

Build a stronger foundation in Python by reviewing fundamental concepts and implementing them in basic machine learning tasks.

Browse courses on Python

Show steps

Follow the 'Python for Data Science and Machine Learning Bootcamp' on Udemy
Practice coding exercises in Python

Review Introduction to Machine Learning Fourth Edition

Show steps

Reinforce your understanding of concepts such as supervised learning, unsupervised learning, and machine learning models by reading this introductory text.

View Machine Learning, revised and updated edition on Amazon

Show steps

Read chapters 1-3
Complete the exercises at the end of each chapter

Solve Python coding challenges

Show steps

Test your coding skills and improve your problem-solving abilities by practicing Python coding challenges on platforms like LeetCode or HackerRank.

Browse courses on Python

Show steps

Solve at least 10 coding challenges
Review the solutions and understand the approaches used

Five other activities

Expand to see all activities and additional details

Show all eight activities

Write a blog post on a machine learning topic

Show steps

Demonstrate your understanding and ability to communicate machine learning concepts by writing a blog post on a specific topic.

Browse courses on Machine Learning

Show steps

Choose a topic and research it thoroughly
Write a well-structured and engaging blog post
Publish your blog post on a relevant platform

Create a data visualization project

Show steps

Demonstrate your understanding of data visualization techniques by creating a data visualization project using tools like Tableau or Power BI.

Browse courses on Data Visualization

Show steps

Choose a dataset and explore it
Create visualizations to represent the data
Write a report summarizing your findings

Build a machine learning model to predict customer churn

Show steps

Apply your machine learning knowledge to a practical problem by building a model to predict customer churn. This project will provide hands-on experience with the entire machine learning workflow.

Browse courses on Machine Learning Projects

Show steps

Collect and prepare a dataset
Train and evaluate different machine learning models
Deploy the best model and monitor its performance

Participate in a machine learning hackathon

Show steps

Challenge yourself and showcase your skills by participating in a machine learning hackathon. This will provide real-world experience and foster collaboration.

Show steps

Join a hackathon platform like Kaggle
Select a competition and build a team
Develop and submit your solution

Mentor junior machine learning enthusiasts

Show steps

Share your knowledge and experience by mentoring junior machine learning enthusiasts. This will reinforce your understanding while supporting the growth of others.

Show steps

Join online forums or communities
Offer guidance and support to others
Participate in Q&A sessions

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Product Manager

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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Research Analyst

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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Consultant

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.

See salaries and explore the career path for Financial Analyst

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Biostatistician

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.

See salaries and explore the career path for Operations Research Analyst