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.
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 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 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.
Aprendizaje no supervisado
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.
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IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
Preguntas Frecuentes
¿Recibiré crédito universitario por completar el programa especializado?
Can I just enroll in a single course?
¿Puedo inscribirme en un solo curso?
¿Cuánto tiempo se necesita para completar un programa especializado?
Do I need to take the courses in a specific order?
Will I earn university credit for completing the Specialization?
¿Recibiré crédito universitario por completar el programa especializado?
Is this course really 100% online? Do I need to attend any classes in person?
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