Learn machine learning through real use cases. Build the skills for a career in one of the most relevant fields of modern AI through hands-on projects and curriculum from IBM’s experts.

4.7

estrellas

196 calificaciones

Joseph Santarcangelo +4 instructores más

4574 ya inscrito

ofrecido por

Programa especializado: IBM Introduction to Machine LearningHabilidades en redes de IBM

Qué aprenderás

Understand the potential applications of machine learning
Gain technical skills like SQL, machine learning modelling, supervised and unsupervised learning, regression, and classification.
Identify opportunities to leverage machine learning in your organization or career
Communicate findings from your machine learning projects to experts and non-experts

Habilidades que obtendrás

Machine Learning Regression
Machine Learning Classification
Machine Learning
Unsupervised Learning
Exploratory Data Analysis
Artificial Intelligence (AI)
Feature Engineering
Statistical Hypothesis Testing
Regression Analysis
Supervised Learning
Linear Regression
Ridge Regression

Acerca de este Programa Especializado

3094 vistas recientes

Machine learning skills are becoming more and more essential in the modern job market. In 2019, Machine Learning Engineer was ranked as the #1 job in the United States, based on the incredible 344% growth of job openings in the field between 2015 to 2018, and the role’s average base salary of $146,085 (Indeed). This four-course Specialization will help you gain the introductory skills to succeed in an in-demand career in machine learning and data science. After completing this program, you’ll be able to realize the potential of machine learning algorithms and artificial intelligence in different business scenarios. You’ll be able to identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes. You’ll also learn how to evaluate your machine learning models and to incorporate best practices. By the end of this program, you will have developed concrete machine learning skills to apply in your workplace or career search, as well as a portfolio of projects demonstrating your proficiency. In addition to receiving a certificate from Coursera, you'll also earn an IBM Badge to help you share your accomplishments with your network and potential employer. You can also leverage the learning from the program to complete the remaining two courses of the six-course IBM Machine Learning Professional Certificate and power a new career in the field of machine learning.

Proyecto de aprendizaje aplicado

In this program, you’ll complete hands-on projects designed to develop your analytical and machine learning skills. You’ll also produce a summary of your insights from each project using data analysis skills, in a similar way as you would in a professional setting, including producing a final presentation to communicate insights to fellow machine learning practitioners, stakeholders, C-suite executives, and chief data officers.
You are highly encouraged to compile your completed projects into an online portfolio that showcases the skills learned in this Specialization.

Certificado para compartir

Obtén un certificado al finalizar

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Cronograma flexible

Establece y mantén fechas de entrega flexibles.

Nivel intermedio

Se requiere cierto nivel de experiencia relacionada.

Aproximadamente 5 meses para completarse

Ritmo sugerido de 4 horas/semana

Inglés (English)

Subtítulos: Inglés (English), Francés (French), Portugués (europeo), Ruso (Russian), Español (Spanish)

Certificado para compartir

Obtén un certificado al finalizar

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Cronograma flexible

Establece y mantén fechas de entrega flexibles.

Nivel intermedio

Se requiere cierto nivel de experiencia relacionada.

Aproximadamente 5 meses para completarse

Ritmo sugerido de 4 horas/semana

Inglés (English)

Subtítulos: Inglés (English), Francés (French), Portugués (europeo), Ruso (Russian), Español (Spanish)

Cómo funciona el programa especializado

Toma cursos

Un programa especializado de Coursera es un conjunto de cursos que te ayudan a dominar una aptitud. Para comenzar, inscríbete en el programa especializado directamente o échale un vistazo a sus cursos y elige uno con el que te gustaría comenzar. Al suscribirte a un curso que forme parte de un programa especializado, quedarás suscrito de manera automática al programa especializado completo. Puedes completar solo un curso: puedes pausar tu aprendizaje o cancelar tu suscripción en cualquier momento. Visita el panel principal del estudiante para realizar un seguimiento de tus inscripciones a cursos y tu progreso.

Proyecto práctico

Cada programa especializado incluye un proyecto práctico. Necesitarás completar correctamente el proyecto para completar el programa especializado y obtener tu certificado. Si el programa especializado incluye un curso separado para el proyecto práctico, necesitarás completar cada uno de los otros cursos antes de poder comenzarlo.

Obtén un certificado

Cuando completes todos los cursos y el proyecto práctico, obtendrás un Certificado que puedes compartir con posibles empleadores y tu red profesional.

Hay 4 cursos en este Programa Especializado

Curso1

Curso 1

Exploratory Data Analysis for Machine Learning

4.6

estrellas

860 calificaciones

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.

By the end of this course you should be able to: Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud Describe and use common feature selection and feature engineering techniques Handle categorical and ordinal features, as well as missing values Use a variety of techniques for detecting and dealing with outliers Articulate why feature scaling is important and use a variety of scaling techniques Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Machine Learning and Artificial Intelligence in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.

Curso2

Curso 2

Supervised Machine Learning: Regression

4.8

estrellas

281 calificaciones

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.

By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Curso3

Curso 3

Supervised Machine Learning: Classification

4.8

estrellas

169 calificaciones

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.

By the end of this course you should be able to: -Differentiate uses and applications of classification and classification ensembles -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models -Describe and use other ensemble methods for classification -Use a variety of error metrics to compare and select the classification model that best suits your data -Use oversampling and undersampling as techniques to handle unbalanced classes in a data set Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Curso4

Curso 4

Unsupervised Machine Learning

4.7

estrellas

143 calificaciones

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.

By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Instructores

ofrecido por

Habilidades en redes de 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?
Si estás suscrito, obtienes una prueba gratis de 7 días, que podrás cancelar cuando desees sin ningún tipo de penalidad. Una vez transcurrido ese tiempo, no realizamos reembolsos. No obstante, puedes cancelar tu suscripción cuando quieras. Consulta nuestra política completa de reembolsos.
¿Puedo inscribirme en un solo curso?
¡Sí! Para empezar, haz clic en la tarjeta del curso que te interesa e inscríbete. Puedes inscribirte y completar el curso para obtener un certificado que puedes compartir o puedes acceder al curso como oyente para ver los materiales del curso de manera gratuita. Cuando cancelas la suscripción de un curso que forma parte de un programa especializado, se cancela automáticamente la suscripción de todo el programa especializado. Visita el panel del estudiante para realizar un seguimiento de tu progreso.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.
¿Puedo tomar este curso de manera gratuita?
Cuando te inscribes en el curso, tienes acceso a todos los cursos del programa especializado y obtienes un certificado cuando completas el trabajo. Si solo deseas leer y ver el contenido del curso, puedes participar del curso como oyente de manera gratuita. Si no puedes pagar la tarifa, puedes solicitar ayuda económica.
¿Este curso es 100 % en línea? ¿Necesito asistir a alguna clase en persona?
Este curso es completamente en línea, de modo que no necesitas ir a un aula en persona. Puedes acceder a tus lecciones, lecturas y tareas en cualquier momento y cualquier lugar a través de Internet o tu dispositivo móvil.
How can I earn my IBM Badge?
Upon completion of this program, you will receive an email from Coursera with directions on how to claim your IBM Badge through Acclaim. Learn more about IBM Badges
What is machine learning?
Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.
What careers can I pursue in the field of machine learning?
Machine learning skills are applicable to a variety of fields, but some jobs that require machine learning skills include:
-Data Scientist
-Machine Learning Engineer
-Software Engineer
In 2019, Machine Learning Engineer was ranked as the #1 job in the United States, based on the incredible 344% growth of job openings in the field between 2015 to 2018, and the role’s average base salary of $146,085 (Indeed). Take advantage of this opportunity to develop your machine learning skills for a high-paying, in-demand career in machine learning today!
¿Cuánto tiempo se necesita para completar un programa especializado?
The entire specialization requires 40-45 hours of study. Each of the 4 courses requires 7-10 hours of study.
What background knowledge is necessary?
Ideally, you should have some background in Math, Stats, and computer programming, as most demonstrations, labs, and projects use Python programming language and concepts like matrix factorization, convergence, or stochastic gradient descent.This Specialization is designed specifically for scientists, software developers, and business analysts who want to round their analytical skills in Data Science, AI, and Machine Learning, but is also appropriate for anyone with a passion for data and basic Math, Statistics, and programming skills.
Do I need to take the courses in a specific order?
We recommend you to take the courses in the order presented in the specialization page, as each course builds on material presented in previous courses.
¿Recibiré crédito universitario por completar el programa especializado?
No.
What will I be able to do upon completing the Specialization?
You will be able to use high-demand Machine Learning techniques in real world data sets. You will be able to derive and communicate insights from data using Exploratory Data Analysis, Supervised Learning, and Unsupervised Learning.

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Programa especializado: IBM Introduction to Machine Learning

Qué aprenderás

Habilidades que obtendrás

Acerca de este Programa Especializado

Proyecto de aprendizaje aplicado