Этот курс входит в специализацию ''Специализация Big Data'
Machine Learning With Big Data
от партнера
Об этом курсе
4.5
Оценки: 945
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
Приобретаемые навыки
Machine Learning ConceptsKnimeMachine LearningApache Spark
Программа курса: что вы изучите
Week
1Welcome
2 видео (всего 14 мин.)
2 видео
Summary of Big Data Integration and Processing10мин
Introduction to Machine Learning with Big Data
7 видео (всего 45 мин.), 7 материалов для самостоятельного изучения, 1 тест
7 видео
Categories Of Machine Learning Techniques7мин
Machine Learning Process3мин
Goals and Activities in the Machine Learning Process10мин
CRISP-DM5мин
Scaling Up Machine Learning Algorithms5мин
Tools Used in this Course5мин
7 материала для самостоятельного изучения
Slides: Machine Learning Overview and Applications25мин
Downloading, Installing and Using KNIMEмин
Downloading and Installing the Cloudera VM Instructions (Windows)10мин
Downloading and Installing the Cloudera VM Instructions (Mac)10мин
Instructions for Downloading Hands On Datasets10мин
Instructions for Starting Jupyter10мин
PDFs of Readings for Week 1 Hands-On10мин
1 практическое упражнение
Machine Learning Overview20мин
Week
2Data Exploration
6 видео (всего 39 мин.), 5 материалов для самостоятельного изучения, 2 тестов
6 видео
Data Terminology4мин
Data Exploration4мин
Data Exploration through Summary Statistics7мин
Data Exploration through Plots8мин
Exploring Data with KNIME Plots9мин
Data Exploration in Spark5мин
5 материала для самостоятельного изучения
Slides: Data Exploration Overview and Terminology10мин
Description of Daily Weather Dataset10мин
Exploring Data with KNIME Plots40мин
Data Exploration in Spark10мин
PDFs of Activities for Data Exploration Hands-On Readings10мин
2 практического упражнения
Data Exploration20мин
Data Exploration in KNIME and Spark Quiz20мин
Data Preparation
8 видео (всего 42 мин.), 4 материалов для самостоятельного изучения, 2 тестов
8 видео
Data Preparation3мин
Data Quality4мин
Addressing Data Quality Issues4мин
Feature Selection5мин
Feature Transformation5мин
Dimensionality Reduction7мин
Handling Missing Values in KNIME5мин
Handling Missing Values in Spark5мин
4 материала для самостоятельного изучения
Slides: Data Preparation for Machine Learning30мин
Handling Missing Values in KNIME20мин
Handling Missing Values in Spark10мин
PDFs for Data Preparation Hands-On Readings10мин
2 практического упражнения
Data Preparation25мин
Handling Missing Values in KNIME and Spark Quiz20мин
Week
3Classification
8 видео (всего 60 мин.), 7 материалов для самостоятельного изучения, 2 тестов
8 видео
Classification4мин
Building and Applying a Classification Model5мин
Classification Algorithms2мин
k-Nearest Neighbors4мин
Decision Trees13мин
Naïve Bayes14мин
Classification using Decision Tree in KNIME8мин
Classification in Spark6мин
7 материала для самостоятельного изучения
Slides: What is Classification?10мин
Slides: Classification Algorithms10мин
Classification using Decision Tree in KNIME45мин
Interpreting a Decision Tree in KNIME20мин
Instructions for Changing the Number of Cloudera VM CPUs10мин
Classification in Spark45мин
PDFs for Classification Hands-On Readings10мин
2 практического упражнения
Classification20мин
Classification in KNIME and Spark Quiz16мин
Week
4Evaluation of Machine Learning Models
7 видео (всего 42 мин.), 7 материалов для самостоятельного изучения, 2 тестов
7 видео
Overfitting in Decision Trees3мин
Using a Validation Set9мин
Metrics to Evaluate Model Performance10мин
Confusion Matrix7мин
Evaluation of Decision Tree in KNIME3мин
Evaluation of Decision Tree in Spark2мин
7 материала для самостоятельного изучения
Slides: Overfitting: What is it and how would you prevent it?10мин
Slides: Model evaluation metrics and methods10мин
Evaluation of Decision Tree in KNIME30мин
Completed KNIME Workflows10мин
Evaluation of Decision Tree in Spark20мин
Comparing Classification Results for KNIME and Spark10мин
PDFs for Evaluation of Machine Learning Models Hands-On Readings10мин
2 практического упражнения
Model Evaluation20мин
Model Evaluation in KNIME and Spark Quiz16мин
4.5
60%
83%
25%
Лучшие рецензии
автор: 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.
автор: 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.
Преподавателя
Mai Nguyen
Lead for Data AnalyticsSan Diego Supercomputer Center
Ilkay Altintas
Chief Data Science OfficerSan Diego Supercomputer Center
О University of California 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.
О специализации ''Big 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.
Часто задаваемые вопросы
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
Остались вопросы? Посетите Центр поддержки учащихся.
Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.
© Coursera Inc., 2018 Все права защищены.
