Acerca de este Curso

85,114 vistas recientes

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 2 de 6 en

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 9 horas para completar

Sugerido: 16 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Habilidades que obtendrás

Data ScienceArtificial Intelligence (AI)Machine LearningBig DataSpark

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 2 de 6 en

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 9 horas para completar

Sugerido: 16 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

2 horas para completar

Week 1: Introduction

2 horas para completar
6 videos (Total 44 minutos), 6 lecturas, 2 cuestionarios
6 videos
What is Big Data?11m
Data storage solutions5m
Parallel data processing strategies of Apache Spark7m
Functional programming basics6m
Resilient Distributed Dataset and DataFrames - ApacheSparkSQL6m
6 lecturas
Course Syllabus10m
Setup of the grading and exercise environment10m
Exercise 1 - working with RDD10m
Exercise 2 - functional programming basics with RDDs10m
Exercise 3 - working with DataFrames10m
Programming Lanuage Options for Apache Spark (optional)10m
2 ejercicios de práctica
Practice Quiz (Ungraded) - Apache Spark concepts8m
Apache Spark and parallel data processing
Semana
2

Semana 2

1 hora para completar

Week 2: Scaling Math for Statistics on Apache Spark

1 hora para completar
5 videos (Total 29 minutos), 1 lectura, 2 cuestionarios
5 videos
Standard deviation3m
Skewness3m
Kurtosis2m
Covariance, Covariance matrices, correlation13m
1 lectura
Exercise 1 - statistics and transfomrations using DataFrames10m
2 ejercicios de práctica
Practice Quiz (Ungraded) - Statistics and API usage on Spark4m
Parallelism in Apache Spark 
Semana
3

Semana 3

1 hora para completar

Week 3: Introduction to Apache SparkML

1 hora para completar
5 videos (Total 34 minutos), 2 lecturas, 3 cuestionarios
5 videos
Introduction to SparkML20m
Extract - Transform - Load3m
Introduction to Clustering: k-Means3m
Using K-Means in Apache SparkML2m
2 lecturas
Exercise 1: Modifying a Apache SparkML Feature Engineering Pipeline10m
Exercise 2 - Working with Clustering and Apache SparkML10m
3 ejercicios de práctica
Practice Quiz (Ungraded) - ML Pipelines4m
SparkML concepts 
Practice Quiz (Ungraded) - SparkML Algorithms
Semana
4

Semana 4

1 hora para completar

Week 4: Supervised and Unsupervised learning with SparkML

1 hora para completar
4 videos (Total 18 minutos), 2 lecturas, 2 cuestionarios
4 videos
LinearRegression with Apache SparkML6m
Logistic Regression1m
LogisticRegression with Apache SparkML4m
2 lecturas
Exercise 1 - Improving Classification performance10m
Course Project10m
2 ejercicios de práctica
Practice Quiz (Ungraded) - SparkML Algorithms (2)4m
Course Project Quiz
4.0
64 revisionesChevron Right

Principales revisiones sobre Scalable Machine Learning on Big Data using Apache Spark

por CLDec 12th 2019

Really really REALLY enjoyed this course! The instructor does a masterful job of going from simple examples and building up complexity in a very logical and thorough way.

por MBDec 20th 2019

Thanks a lot for helping me. I would suggest that the data storage in IBM cloud should be described in detail.

Instructor

Calificación del instructor3.89/5 (20 calificaciones)Info
Imagen del instructor, Romeo Kienzler

Romeo Kienzler 

Chief Data Scientist, Course Lead
IBM Watson IoT
53,524 alumnos
5 cursos

ofrecido por

Logotipo de IBM

IBM

Acerca de Certificado profesional de IBM AI Engineering

The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning. You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers. You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders. By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer....
IBM AI Engineering

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos del Certificado y te darán un certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.