- Data Science
- Deep Learning
- Artificial Intelligence (AI)
- Machine Learning
- Python Programming
- Feature Engineering
- Statistical Hypothesis Testing
- Exploratory Data Analysis
- Regression Analysis
- Supervised Learning
- Linear Regression
- Ridge Regression
Certificado profesional de Aprendizaje automático de IBM
Machine Learning, Time Series & Survival Analysis. Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Also gain practice in specialized topics such as Time Series Analysis and Survival Analysis.
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Habilidades que obtendrás
Acerca de este Certificado profesional
Proyecto de aprendizaje aplicado
This Professional Certificate has a strong emphasis on developing the skills that help you advance a career in Machine Learning. All the courses include a series of hands-on labs and final projects that help you focus on a specific project that interests you. Throughout this Professional Certificate, you will gain exposure to a series of tools, libraries, cloud services, datasets, algorithms, assignments and projects that will provide you with practical skills with applicability to Machine Learning jobs. These skills include:
Tools: Jupyter Notebooks and Watson Studio
Libraries: Pandas, NumPy, Matplotlib, Seaborn, ipython-sql, Scikit-learn, ScipPy, Keras, and TensorFlow.
Se requiere cierto nivel de experiencia relacionada.
Se requiere cierto nivel de experiencia relacionada.
¿Qué es un certificado profesional?
Desarrolla las habilidades necesarias para completar el trabajo
Ya sea que desees comenzar una nueva carrera o cambiar tu carrera actual, los certificados profesionales de Coursera te ayudan a prepararte para el puesto. Aprende a tu propio ritmo, en el momento y el lugar que te resulten más cómodos. Inscríbete hoy mismo y descubre una nueva carrera con una prueba gratuita de 7 días. Puedes pausar tus clases o finalizar la suscripción en cualquier momento.
Proyectos prácticos
Aplica tus habilidades en proyectos prácticos y desarrolla una cartera que demuestre tu preparación para los trabajos a los posibles empleadores. Deberás terminar los proyectos correctamente para obtener tu certificado.
Obtén una credencial profesional
Cuando completas todos los cursos del programa, obtienes un certificado que puedes compartir con tu red profesional, así como acceso a los recursos de apoyo profesional que te ayudarán a comenzar tu nueva carrera. Muchos certificados profesionales tienen socios interesados en contratar personal que reconocen la credencial del certificado profesional, y otros pueden ayudarte en tu preparación para el examen de un certificado. Puedes ver más información en las páginas del certificado profesional particular en donde aplica.

Hay 6 cursos en este Certificado profesional
Exploratory Data Analysis for Machine Learning
This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.
Supervised Machine Learning: Regression
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
Supervised Machine Learning: Classification
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.
Unsupervised Machine Learning
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.
ofrecido por

IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Preguntas Frecuentes
¿Cuál es la política de reembolsos?
¿Puedo inscribirme en un solo curso?
¿Cuánto tiempo se necesita para completar un programa especializado?
What background knowledge is necessary?
Do I need to take the courses in a specific order?
¿Recibiré crédito universitario por completar el programa especializado?
What will I be able to do upon completing the Specialization?
¿Este curso es 100 % en línea? ¿Necesito asistir a alguna clase en persona?
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