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Joseph Santarcangelo
Este curso mergulha nos fundamentos básicos de aprendizado de máquina usando uma linguagem de programação acessível e bem conhecida, Python. Neste curso, revisaremos dois componentes principais: Primeiro, você aprenderá sobre o propósito do aprendizado de máquina e onde ele se aplica no mundo real. Segundo, você terá uma visão geral dos tópicos de aprendizado de máquina, como um aprendizado supervisionado versus não supervisionado, avaliação de modelo e algoritmos de aprendizado de máquina. Neste curso, você praticará com exemplos de aprendizado de máquina da vida real e verá como ele a afeta a sociedade de maneiras que você...
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Este curso mergulha nos fundamentos básicos de aprendizado de máquina usando uma linguagem de programação acessível e bem conhecida, Python. Neste curso, revisaremos dois componentes principais: Primeiro, você aprenderá sobre o propósito do aprendizado de máquina e onde ele se aplica no mundo real. Segundo, você terá uma visão geral dos tópicos de aprendizado de máquina, como um aprendizado supervisionado versus não supervisionado, avaliação de modelo e algoritmos de aprendizado de máquina. Neste curso, você praticará com exemplos de aprendizado de máquina da vida real e verá como ele a afeta a sociedade de maneiras que você nunca imaginou! Veja o que você terá durante as próximas semanas dedicando algumas horas por semana. 1) Novas habilidades para acrescentar em seu currículo, tais como regressão, classificação, clusterização, aprendizado sci-kit e SciPy 2) Novos projetos que você pode acrescentar ao seu portfólio, incluindo detecção de câncer, previsão de tendências econômicas, previsão de churn de cliente, máquinas de recomendação e muito mais. 3) E um certificado em aprendizado de máquina para comprovar a sua competência e compartilhar onde você quiser, online ou offline, como no perfil do LinkedIn e nas redes sociais. Se você escolher fazer este curso e obter o certificado do curso do Coursera, você também receberá um selo digital IBM após a conclusão bem-sucedida do curso.
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops data science skills, knowledge, and tools, which are in high demand in the job market
Emphasizes applications of machine learning in real-world scenarios
Provides a structured and accessible introduction to machine learning
Taught by experienced instructors with a strong reputation in the field
Offers hands-on projects and examples that help reinforce learning
Provides a certificate upon completion, which can enhance credibility

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Reviews summary

Python para aprendizado de máquina

Com base em 1 avaliação, este curso é recebido negativamente. Os alunos expressam insatisfação com o projeto final.

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 Aprendizado de máquina com Python with these activities:
Course Notes Consolidation
Review and synthesize course notes, assignments, and other materials to enhance understanding and retention.
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  • Gather and organize all relevant course materials.
  • Review and identify key concepts, definitions, and important examples.
  • Create a comprehensive set of notes that summarizes the course content.
Python Refresher
Review the basics of Python syntax and data structures to ensure a solid foundation for the course.
Browse courses on Python
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  • Review Python syntax and data structures using online tutorials.
  • Solve coding problems on platforms like LeetCode or HackerRank.
Peer Tutoring
Help other learners understand course concepts and complete assignments by providing support and guidance.
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  • Join or create a study group to assist peers with course material.
  • Offer one-on-one tutoring sessions to clarify concepts and answer questions.
Four other activities
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Scikit-learn Tutorial
Work through guided tutorials on using Scikit-learn for machine learning tasks to enhance practical skills.
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  • Follow online tutorials to learn the basics of Scikit-learn.
  • Apply Scikit-learn to solve machine learning problems in practice.
Kaggle Competition
Participate in a Kaggle competition to test machine learning skills, learn from others, and improve problem-solving abilities.
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Show steps
  • Identify a relevant Kaggle competition that aligns with course topics.
  • Form a team or work individually to develop a model.
  • Apply machine learning techniques to solve the competition problem.
  • Submit the solution and track progress on the leaderboard.
Regularization Techniques Practice
Practice applying regularization techniques to machine learning models to improve performance and prevent overfitting.
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  • Study different regularization techniques, such as L1 and L2.
  • Implement regularization techniques in code and compare their effects.
Machine Learning Project
Develop and present a machine learning project that demonstrates proficiency in the course concepts and practical skills.
Show steps
  • Identify a problem or dataset to apply machine learning techniques.
  • Design and implement a machine learning model to solve the problem.
  • Test and evaluate the model's performance.
  • Present the project findings and code in a portfolio or technical report.

Career center

Learners who complete Aprendizado de máquina com Python will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to uncover meaningful insights and trends. They use their knowledge of machine learning algorithms and statistical modeling to build predictive models and make data-driven decisions. The "Aprendizado de máquina com Python" course provides a solid foundation in machine learning concepts and techniques, which are essential for success in this role.
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. They work closely with Data Scientists to translate business requirements into technical solutions. The "Aprendizado de máquina com Python" course provides a comprehensive overview of machine learning algorithms and techniques, as well as hands-on experience in building and deploying models using Python.
Software Engineer
Software Engineers design, develop, and test software applications. They use their knowledge of programming languages and software development tools to create innovative solutions to business problems. The "Aprendizado de máquina com Python" course may be useful for Software Engineers who want to incorporate machine learning capabilities into their applications.
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use their knowledge of statistical methods and data visualization tools to communicate insights to stakeholders. The "Aprendizado de máquina com Python" course can help Data Analysts build a foundation in machine learning, which can enhance their ability to analyze data and make predictions.
Business Analyst
Business Analysts identify and analyze business needs and develop solutions to improve efficiency and effectiveness. They use their knowledge of business processes and data analysis techniques to make recommendations and implement changes. The "Aprendizado de máquina com Python" course may be useful for Business Analysts who want to gain a better understanding of machine learning and its potential applications in the business world.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They use their knowledge of machine learning algorithms and statistical modeling to develop trading strategies and risk management systems. The "Aprendizado de máquina com Python" course provides a solid foundation in machine learning concepts and techniques, which are essential for success in this role.
Data Engineer
Data Engineers design, build, and maintain the infrastructure that stores and processes data. They use their knowledge of big data technologies and data management best practices to ensure that data is accessible, reliable, and secure. The "Aprendizado de máquina com Python" course may be useful for Data Engineers who want to gain a better understanding of machine learning and its potential applications in data engineering.
Product Manager
Product Managers are responsible for defining the vision and roadmap for a product. They work closely with engineers, designers, and marketing teams to bring products to market. The "Aprendizado de máquina com Python" course can help Product Managers gain a better understanding of machine learning and its potential applications in product development.
Statistician
Statisticians collect, analyze, and interpret data to draw conclusions about the world around us. They use their knowledge of statistical methods and data analysis techniques to solve problems in a variety of fields, including healthcare, finance, and marketing. The "Aprendizado de máquina com Python" course provides a solid foundation in machine learning concepts and techniques, which can be useful for Statisticians who want to expand their skillset.
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to optimize business processes and operations. They use their knowledge of machine learning algorithms and statistical modeling to develop solutions to problems in areas such as supply chain management, logistics, and healthcare. The "Aprendizado de máquina com Python" course provides a solid foundation in machine learning concepts and techniques, which are essential for success in this role.
Financial Analyst
Financial Analysts use financial data and analysis to make investment recommendations and decisions. They use their knowledge of financial markets and accounting principles to evaluate the performance of companies and make recommendations to clients. The "Aprendizado de máquina com Python" course may be useful for Financial Analysts who want to gain a better understanding of machine learning and its potential applications in financial analysis.
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They use their knowledge of insurance, finance, and mathematics to develop solutions to problems in areas such as life insurance, health insurance, and pensions. The "Aprendizado de máquina com Python" course provides a solid foundation in machine learning concepts and techniques, which can be useful for Actuaries who want to expand their skillset.
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior and trends. They use their knowledge of research methods and data analysis techniques to make recommendations to businesses on how to improve their products and services. The "Aprendizado de máquina com Python" course may be useful for Market Researchers who want to gain a better understanding of machine learning and its potential applications in market research.
Web Developer
Web Developers design and develop websites and web applications. They use their knowledge of programming languages and web development tools to create user-friendly and interactive experiences. The "Aprendizado de máquina com Python" course may be useful for Web Developers who want to incorporate machine learning capabilities into their applications.
Database Administrator
Database Administrators design, implement, and maintain databases. They use their knowledge of database management systems and data management best practices to ensure that data is accessible, reliable, and secure. The "Aprendizado de máquina com Python" course may be useful for Database Administrators who want to gain a better understanding of machine learning and its potential applications in database management.

Reading list

We've selected six 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 Aprendizado de máquina com Python.
Covers topics such as data preprocessing, model selection, and evaluation using Python libraries like Scikit-Learn, Keras, and TensorFlow and provides a detailed overview of supervised and unsupervised learning. It more advanced resource that would be suitable for learners with some background in machine learning.
Provides a comprehensive overview of machine learning with Python, covering topics like supervised and unsupervised learning, model selection, and evaluation. It practical and hands-on resource suitable for learners with some prior knowledge of Python.
Provides a practical and hands-on introduction to machine learning, covering topics like supervised and unsupervised learning, model selection, and evaluation. It concise and engaging resource suitable for learners with little to no prior knowledge of machine learning.
Provides a practical and hands-on introduction to deep learning with Fastai and PyTorch, covering topics like convolutional neural networks, recurrent neural networks, and generative models. It beginner-friendly resource suitable for learners with little to no prior knowledge of deep learning.
Provides a theoretical foundation for machine learning, covering topics like probability, Bayesian inference, and graphical models. It comprehensive and advanced resource suitable for learners interested in the theoretical underpinnings of machine learning.
Provides a comprehensive overview of deep learning and neural networks, covering topics like convolutional neural networks, recurrent neural networks, and generative models. It highly technical and advanced resource suitable for learners with a strong background in machine learning and mathematics.

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