Об этом курсе: 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
Для кого этот курс: This course is for those new to data science. Completion of “Big Data Integration and Processing” is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications.
Автор: University of California San Diego
Преподаватели: Mai Nguyen, Lead for Data Analytics
San Diego Supercomputer Center
Преподаватели: Ilkay Altintas, Chief Data Science Officer
San Diego Supercomputer Center
| Основные сведения | Курс 4 из 6 — Big Data Specialization |
| Выполнение | 5 Weeks, 3 - 5 hours per week |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
Welcome
2 видео
- Видео: Summary of Big Data Integration and Processing
- Discussion Prompt: Getting to Know You: Tell us about yourself and why you are taking this course.
- Discussion Prompt: Discussion Forum for Course Content Issues
Introduction to Machine Learning with Big Data
7 видео, 7 материала для самостоятельного изучения
- Видео: Categories Of Machine Learning Techniques
- Reading: Slides: Machine Learning Overview and Applications
- Discussion Prompt: Machine Learning in Everyday Life
- Видео: Machine Learning Process
- Видео: Goals and Activities in the Machine Learning Process
- Видео: CRISP-DM
- Видео: Scaling Up Machine Learning Algorithms
- Видео: Tools Used in this Course
- Reading: Downloading, Installing and Using KNIME
- Reading: Downloading and Installing the Cloudera VM Instructions (Windows)
- Reading: Downloading and Installing the Cloudera VM Instructions (Mac)
- Reading: Instructions for Downloading Hands On Datasets
- Reading: Instructions for Starting Jupyter
- Reading: PDFs of Readings for Week 1 Hands-On
Оцениваемый: Machine Learning Overview
НЕДЕЛЯ 2
Data Exploration
6 видео, 5 материала для самостоятельного изучения
- Видео: Data Exploration
- Видео: Data Exploration through Summary Statistics
- Видео: Data Exploration through Plots
- Discussion Prompt: What's Wrong with Pie Charts?
- Reading: Slides: Data Exploration Overview and Terminology
- Reading: Description of Daily Weather Dataset
- Reading: Exploring Data with KNIME Plots
- Видео: Exploring Data with KNIME Plots
- Reading: Data Exploration in Spark
- Видео: Data Exploration in Spark
- Reading: PDFs of Activities for Data Exploration Hands-On Readings
Оцениваемый: Data Exploration
Оцениваемый: Data Exploration in KNIME and Spark Quiz
Data Preparation
8 видео, 4 материала для самостоятельного изучения
- Видео: Data Quality
- Discussion Prompt: Quality Issues with Real Data
- Видео: Addressing Data Quality Issues
- Видео: Feature Selection
- Видео: Feature Transformation
- Видео: Dimensionality Reduction
- Discussion Prompt: Domain Knowledge in Data Preparation
- Reading: Slides: Data Preparation for Machine Learning
- Reading: Handling Missing Values in KNIME
- Видео: Handling Missing Values in KNIME
- Reading: Handling Missing Values in Spark
- Видео: Handling Missing Values in Spark
- Reading: PDFs for Data Preparation Hands-On Readings
Оцениваемый: Data Preparation
Оцениваемый: Handling Missing Values in KNIME and Spark Quiz
НЕДЕЛЯ 3
Classification
8 видео, 7 материала для самостоятельного изучения
- Видео: Building and Applying a Classification Model
- Reading: Slides: What is Classification?
- Видео: Classification Algorithms
- Видео: k-Nearest Neighbors
- Видео: Decision Trees
- Видео: Naïve Bayes
- Reading: Slides: Classification Algorithms
- Reading: Classification using Decision Tree in KNIME
- Видео: Classification using Decision Tree in KNIME
- Reading: Interpreting a Decision Tree in KNIME
- Reading: Instructions for Changing the Number of Cloudera VM CPUs
- Reading: Classification in Spark
- Видео: Classification in Spark
- Discussion Prompt: Why Exclude Relative Humidity?
- Reading: PDFs for Classification Hands-On Readings
Оцениваемый: Classification
Оцениваемый: Classification in KNIME and Spark Quiz
НЕДЕЛЯ 4
Evaluation of Machine Learning Models
7 видео, 7 материала для самостоятельного изучения
- Видео: Overfitting in Decision Trees
- Видео: Using a Validation Set
- Reading: Slides: Overfitting: What is it and how would you prevent it?
- Видео: Metrics to Evaluate Model Performance
- Видео: Confusion Matrix
- Discussion Prompt: Model Interpretability vs. Accuracy
- Reading: Slides: Model evaluation metrics and methods
- Reading: Evaluation of Decision Tree in KNIME
- Видео: Evaluation of Decision Tree in KNIME
- Reading: Completed KNIME Workflows
- Reading: Evaluation of Decision Tree in Spark
- Видео: Evaluation of Decision Tree in Spark
- Reading: Comparing Classification Results for KNIME and Spark
- Reading: PDFs for Evaluation of Machine Learning Models Hands-On Readings
Оцениваемый: Model Evaluation
Оцениваемый: Model Evaluation in KNIME and Spark Quiz
НЕДЕЛЯ 5
Regression, Cluster Analysis, and Association Analysis
8 видео, 6 материала для самостоятельного изучения
- Видео: Linear Regression
- Reading: Slides: Regression
- Видео: Cluster Analysis
- Видео: k-Means Clustering
- Reading: Slides: Cluster Analysis
- Discussion Prompt: Clustering Applications
- Видео: Association Analysis
- Видео: Association Analysis in Detail
- Reading: Slides: Association Analysis
- Discussion Prompt: Applications of Association Analysis
- Видео: Machine Learning With Big Data - Final Remarks
- Reading: Description of Minute Weather Dataset
- Reading: Cluster Analysis in Spark
- Видео: Cluster Analysis in Spark
- Reading: PDFs of Cluster Analysis in Spark Hands-On Readings
Оцениваемый: Regression, Cluster Analysis, & Association Analysis
Оцениваемый: Cluster Analysis in Spark Quiz
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Авторы
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.
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Рейтинги и отзывы
This is a very good introductory course that benefits from excellent teaching and materials. It is highly recommended.
The course was really informative .I got new things to learn.
very good overview of machine learning. Hands on exercises gives a solid platform to explore the real problems. Realize that ML is all about experiments doing , no theory. More u get ur hands dirty more u learn
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Great and very practical introduction to machine learning, good explanations. Thanks Mai...