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
Ce cours fait partie de la Spécialisation Big Data
Machine Learning With Big Data
proposé par
À propos de ce cours
4.5
1,013 notes
Compétences que vous acquerrez
Machine Learning ConceptsKnimeMachine LearningApache Spark
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
1Welcome
2 videos (Total 14 min)
2 vidéos
Summary of Big Data Integration and Processing10 min
Introduction to Machine Learning with Big Data
7 videos (Total 45 min), 7 lectures, 1 quiz
7 vidéos
Categories Of Machine Learning Techniques7 min
Machine Learning Process3 min
Goals and Activities in the Machine Learning Process10 min
CRISP-DM5 min
Scaling Up Machine Learning Algorithms5 min
Tools Used in this Course5 min
7 lectures
Slides: Machine Learning Overview and Applications25 min
Downloading, Installing and Using KNIME min
Downloading and Installing the Cloudera VM Instructions (Windows)10 min
Downloading and Installing the Cloudera VM Instructions (Mac)10 min
Instructions for Downloading Hands On Datasets10 min
Instructions for Starting Jupyter10 min
PDFs of Readings for Week 1 Hands-On10 min
1 exercices pour s'entraîner
Machine Learning Overview20 min
Semaine
2Data Exploration
6 videos (Total 39 min), 5 lectures, 2 quiz
6 vidéos
Data Terminology4 min
Data Exploration4 min
Data Exploration through Summary Statistics7 min
Data Exploration through Plots8 min
Exploring Data with KNIME Plots9 min
Data Exploration in Spark5 min
5 lectures
Slides: Data Exploration Overview and Terminology10 min
Description of Daily Weather Dataset10 min
Exploring Data with KNIME Plots40 min
Data Exploration in Spark10 min
PDFs of Activities for Data Exploration Hands-On Readings10 min
2 exercices pour s'entraîner
Data Exploration20 min
Data Exploration in KNIME and Spark Quiz20 min
Data Preparation
8 videos (Total 42 min), 4 lectures, 2 quiz
8 vidéos
Data Preparation3 min
Data Quality4 min
Addressing Data Quality Issues4 min
Feature Selection5 min
Feature Transformation5 min
Dimensionality Reduction7 min
Handling Missing Values in KNIME5 min
Handling Missing Values in Spark5 min
4 lectures
Slides: Data Preparation for Machine Learning30 min
Handling Missing Values in KNIME20 min
Handling Missing Values in Spark10 min
PDFs for Data Preparation Hands-On Readings10 min
2 exercices pour s'entraîner
Data Preparation25 min
Handling Missing Values in KNIME and Spark Quiz20 min
Semaine
3Classification
8 videos (Total 60 min), 7 lectures, 2 quiz
8 vidéos
Classification4 min
Building and Applying a Classification Model5 min
Classification Algorithms2 min
k-Nearest Neighbors4 min
Decision Trees13 min
Naïve Bayes14 min
Classification using Decision Tree in KNIME8 min
Classification in Spark6 min
7 lectures
Slides: What is Classification?10 min
Slides: Classification Algorithms10 min
Classification using Decision Tree in KNIME45 min
Interpreting a Decision Tree in KNIME20 min
Instructions for Changing the Number of Cloudera VM CPUs10 min
Classification in Spark45 min
PDFs for Classification Hands-On Readings10 min
2 exercices pour s'entraîner
Classification20 min
Classification in KNIME and Spark Quiz16 min
Semaine
4Evaluation of Machine Learning Models
7 videos (Total 42 min), 7 lectures, 2 quiz
7 vidéos
Overfitting in Decision Trees3 min
Using a Validation Set9 min
Metrics to Evaluate Model Performance10 min
Confusion Matrix7 min
Evaluation of Decision Tree in KNIME3 min
Evaluation of Decision Tree in Spark2 min
7 lectures
Slides: Overfitting: What is it and how would you prevent it?10 min
Slides: Model evaluation metrics and methods10 min
Evaluation of Decision Tree in KNIME30 min
Completed KNIME Workflows10 min
Evaluation of Decision Tree in Spark20 min
Comparing Classification Results for KNIME and Spark10 min
PDFs for Evaluation of Machine Learning Models Hands-On Readings10 min
2 exercices pour s'entraîner
Model Evaluation20 min
Model Evaluation in KNIME and Spark Quiz16 min
4.5
195 avis60%
a commencé une nouvelle carrière après avoir terminé ces cours
83%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
25%
a obtenu une augmentation de salaire ou une promotion
Meilleurs avis
par 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.
par RC•Sep 1st 2018
Amazing training on ML for people starting their first experiences with the topic. Practical and easy to understand examples that can be further extended by the student.
Enseignants
Mai Nguyen
Lead for Data AnalyticsSan Diego Supercomputer Center
Ilkay Altintas
Chief Data Science OfficerSan Diego Supercomputer Center
À propos de 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.
À propos de la Spécialisation 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.
Foire Aux Questions
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