Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.
At the end of the course, you will be able to:
• Design an approach to leverage data using the steps in the machine learning process.
• Apply machine learning techniques to explore and prepare data for modeling.
• Identify the type of machine learning problem in order to apply the appropriate set of techniques.
• Construct models that learn from data using widely available open source tools.
• Analyze big data problems using scalable machine learning algorithms on Spark.
Software Requirements:
Cloudera VM, KNIME, Spark
Este curso faz parte do Programa de cursos integrados Big DataBig Data
Aprendizado de máquina com Big Data
oferecido por
Informações sobre o curso
4.5
1,099 classificações
Habilidades que você terá
Machine Learning ConceptsKnimeMachine LearningApache Spark
Programa - O que você aprenderá com este curso
Semana
124 minutos para concluir
Welcome
2 vídeos (total de (Total 14 mín.) min)
2 videos
Summary of Big Data Integration and Processing10min
3 horas para concluir
Introduction to Machine Learning with Big Data
7 vídeos (total de (Total 45 mín.) min), 7 leituras, 1 teste
7 videos
Categories Of Machine Learning Techniques7min
Machine Learning Process3min
Goals and Activities in the Machine Learning Process10min
CRISP-DM5min
Scaling Up Machine Learning Algorithms5min
Tools Used in this Course5min
7 leituras
Slides: Machine Learning Overview and Applications25min
Downloading, Installing and Using KNIMEs
Downloading and Installing the Cloudera VM Instructions (Windows)10min
Downloading and Installing the Cloudera VM Instructions (Mac)10min
Instructions for Downloading Hands On Datasets10min
Instructions for Starting Jupyter10min
PDFs of Readings for Week 1 Hands-On10min
1 exercício prático
Machine Learning Overview20min
Semana
23 horas para concluir
Data Exploration
6 vídeos (total de (Total 39 mín.) min), 5 leituras, 2 testes
6 videos
Data Terminology4min
Data Exploration4min
Data Exploration through Summary Statistics7min
Data Exploration through Plots8min
Exploring Data with KNIME Plots9min
Data Exploration in Spark5min
5 leituras
Slides: Data Exploration Overview and Terminology10min
Description of Daily Weather Dataset10min
Exploring Data with KNIME Plots40min
Data Exploration in Spark10min
PDFs of Activities for Data Exploration Hands-On Readings10min
2 exercícios práticos
Data Exploration20min
Data Exploration in KNIME and Spark Quiz20min
3 horas para concluir
Data Preparation
8 vídeos (total de (Total 42 mín.) min), 4 leituras, 2 testes
8 videos
Data Preparation3min
Data Quality4min
Addressing Data Quality Issues4min
Feature Selection5min
Feature Transformation5min
Dimensionality Reduction7min
Handling Missing Values in KNIME5min
Handling Missing Values in Spark5min
4 leituras
Slides: Data Preparation for Machine Learning30min
Handling Missing Values in KNIME20min
Handling Missing Values in Spark10min
PDFs for Data Preparation Hands-On Readings10min
2 exercícios práticos
Data Preparation25min
Handling Missing Values in KNIME and Spark Quiz20min
Semana
34 horas para concluir
Classification
8 vídeos (total de (Total 60 mín.) min), 7 leituras, 2 testes
8 videos
Classification4min
Building and Applying a Classification Model5min
Classification Algorithms2min
k-Nearest Neighbors4min
Decision Trees13min
Naïve Bayes14min
Classification using Decision Tree in KNIME8min
Classification in Spark6min
7 leituras
Slides: What is Classification?10min
Slides: Classification Algorithms10min
Classification using Decision Tree in KNIME45min
Interpreting a Decision Tree in KNIME20min
Instructions for Changing the Number of Cloudera VM CPUs10min
Classification in Spark45min
PDFs for Classification Hands-On Readings10min
2 exercícios práticos
Classification20min
Classification in KNIME and Spark Quiz16min
Semana
43 horas para concluir
Evaluation of Machine Learning Models
7 vídeos (total de (Total 42 mín.) min), 7 leituras, 2 testes
7 videos
Overfitting in Decision Trees3min
Using a Validation Set9min
Metrics to Evaluate Model Performance10min
Confusion Matrix7min
Evaluation of Decision Tree in KNIME3min
Evaluation of Decision Tree in Spark2min
7 leituras
Slides: Overfitting: What is it and how would you prevent it?10min
Slides: Model evaluation metrics and methods10min
Evaluation of Decision Tree in KNIME30min
Completed KNIME Workflows10min
Evaluation of Decision Tree in Spark20min
Comparing Classification Results for KNIME and Spark10min
PDFs for Evaluation of Machine Learning Models Hands-On Readings10min
2 exercícios práticos
Model Evaluation20min
Model Evaluation in KNIME and Spark Quiz16min
56%
comecei uma nova carreira após concluir estes cursos
48%
consegui um benefício significativo de carreira com este curso
15%
recebi um aumento ou promoção
Melhores avaliações
por PT•Jan 9th 2017
The course was the best introduction I had for machine learning. Helped me a lot to understand different concepts from people who already know about the subject and I didn't have any idea.
por PR•Jul 19th 2018
Excellent course, I learned a lot about machine learning with big data, but most importantly I feel ready to take it into more complex level although I realized there is lots to learn.
Instrutores
Mai Nguyen
Lead for Data AnalyticsSan Diego Supercomputer Center
Ilkay Altintas
Chief Data Science OfficerSan Diego Supercomputer Center
Sobre Universidade da Califórnia, San Diego
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
Sobre o Programa de cursos integrados Big DataBig Data
Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.
*********
Do you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
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.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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