Informações sobre o curso
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Curso 2 de 6 no

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 11 horas para completar

Sugerido: 20 hours/week...

Inglês

Legendas: Inglês

Habilidades que você terá

Data ScienceArtificial Intelligence (AI)Machine LearningBig DataSpark

Curso 2 de 6 no

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 11 horas para completar

Sugerido: 20 hours/week...

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
2 horas para concluir

Week 1: Introduction

6 vídeos (Total 44 mín.), 5 leituras, 2 testes
6 videos
What is Big Data?11min
Data storage solutions5min
Parallel data processing strategies of Apache Spark7min
Functional programming basics6min
Resilient Distributed Dataset and DataFrames - ApacheSparkSQL6min
5 leituras
Course Syllabus10min
Setup of the grading and exercise environment10min
Exercise 1 - working with RDD10min
Exercise 2 - functional programming basics with RDDs10min
Exercise 3 - working with DataFrames10min
2 exercícios práticos
Practice Quiz (Ungraded) - Apache Spark concepts8min
Apache Spark and parallel data processing
Semana
2
1 hora para concluir

Week 2: Scaling Math for Statistics on Apache Spark

5 vídeos (Total 29 mín.), 1 leitura, 2 testes
5 videos
Standard deviation3min
Skewness3min
Kurtosis2min
Covariance, Covariance matrices, correlation13min
1 leituras
Exercise 1 - statistics and transfomrations using DataFrames10min
2 exercícios práticos
Practice Quiz (Ungraded) - Statistics and API usage on Spark4min
Parallelism in Apache Spark 
Semana
3
1 hora para concluir

Week 3: Introduction to Apache SparkML

5 vídeos (Total 34 mín.), 2 leituras, 3 testes
5 videos
Introduction to SparkML20min
Extract - Transform - Load3min
Introduction to Clustering: k-Means3min
Using K-Means in Apache SparkML2min
2 leituras
Exercise 1: Modifying a Apache SparkML Feature Engineering Pipeline10min
Exercise 2 - Working with Clustering and Apache SparkML10min
3 exercícios práticos
Practice Quiz (Ungraded) - ML Pipelines4min
SparkML concepts 
Practice Quiz (Ungraded) - SparkML Algorithms
Semana
4
1 hora para concluir

Week 4: Supervised and Unsupervised learning with SparkML

4 vídeos (Total 18 mín.), 2 leituras, 2 testes
4 videos
LinearRegression with Apache SparkML6min
Logistic Regression1min
LogisticRegression with Apache SparkML4min
2 leituras
Exercise 1 - Improving Classification performance10min
Course Project10min
2 exercícios práticos
Practice Quiz (Ungraded) - SparkML Algorithms (2)4min
Course Project Quiz
4.1
6 avaliaçõesChevron Right

Principais avaliações do Scalable Machine Learning on Big Data using Apache Spark

por ATSep 24th 2019

In very simple and crisp way a lot of details are covered about Apache Spark. Very good way to start.

por WOSep 30th 2019

Great tutor, he loves to keep things simple and to the point. Loved the course.

Instrutores

Avatar

Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT

Sobre 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....

Sobre Certificado Profissional 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

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve em um curso, ganha acesso a todos os cursos no certificado e obtém um certificado quando completar o trabalho. Seu certificado eletrônico pode ser adicionado à sua página de Participações, de onde você pode imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, pode participar como ouvinte sem custo.

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