À propos de ce cours : 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
Les destinataires de ce cours : 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.
Créé par : University of California San Diego
Enseigné par : Mai Nguyen, Lead for Data Analytics
San Diego Supercomputer Center
Enseigné par : Ilkay Altintas, Chief Data Science Officer
San Diego Supercomputer Center
| Informations de base | Cours 4 de 6 dans Big Data Specialization |
| Engagement | 5 Weeks, 3 - 5 hours per week |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
Welcome
2 vidéos
- Vidéo: 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 vidéos, 7 lectures
- Vidéo: Categories Of Machine Learning Techniques
- Reading: Slides: Machine Learning Overview and Applications
- Discussion Prompt: Machine Learning in Everyday Life
- Vidéo: Machine Learning Process
- Vidéo: Goals and Activities in the Machine Learning Process
- Vidéo: CRISP-DM
- Vidéo: Scaling Up Machine Learning Algorithms
- Vidéo: 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
Noté: Machine Learning Overview
SEMAINE 2
Data Exploration
6 vidéos, 5 lectures
- Vidéo: Data Exploration
- Vidéo: Data Exploration through Summary Statistics
- Vidéo: 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
- Vidéo: Exploring Data with KNIME Plots
- Reading: Data Exploration in Spark
- Vidéo: Data Exploration in Spark
- Reading: PDFs of Activities for Data Exploration Hands-On Readings
Noté: Data Exploration
Noté: Data Exploration in KNIME and Spark Quiz
Data Preparation
8 vidéos, 4 lectures
- Vidéo: Data Quality
- Discussion Prompt: Quality Issues with Real Data
- Vidéo: Addressing Data Quality Issues
- Vidéo: Feature Selection
- Vidéo: Feature Transformation
- Vidéo: Dimensionality Reduction
- Discussion Prompt: Domain Knowledge in Data Preparation
- Reading: Slides: Data Preparation for Machine Learning
- Reading: Handling Missing Values in KNIME
- Vidéo: Handling Missing Values in KNIME
- Reading: Handling Missing Values in Spark
- Vidéo: Handling Missing Values in Spark
- Reading: PDFs for Data Preparation Hands-On Readings
Noté: Data Preparation
Noté: Handling Missing Values in KNIME and Spark Quiz
SEMAINE 3
Classification
8 vidéos, 7 lectures
- Vidéo: Building and Applying a Classification Model
- Reading: Slides: What is Classification?
- Vidéo: Classification Algorithms
- Vidéo: k-Nearest Neighbors
- Vidéo: Decision Trees
- Vidéo: Naïve Bayes
- Reading: Slides: Classification Algorithms
- Reading: Classification using Decision Tree in KNIME
- Vidéo: 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
- Vidéo: Classification in Spark
- Discussion Prompt: Why Exclude Relative Humidity?
- Reading: PDFs for Classification Hands-On Readings
Noté: Classification
Noté: Classification in KNIME and Spark Quiz
SEMAINE 4
Evaluation of Machine Learning Models
7 vidéos, 7 lectures
- Vidéo: Overfitting in Decision Trees
- Vidéo: Using a Validation Set
- Reading: Slides: Overfitting: What is it and how would you prevent it?
- Vidéo: Metrics to Evaluate Model Performance
- Vidéo: Confusion Matrix
- Discussion Prompt: Model Interpretability vs. Accuracy
- Reading: Slides: Model evaluation metrics and methods
- Reading: Evaluation of Decision Tree in KNIME
- Vidéo: Evaluation of Decision Tree in KNIME
- Reading: Completed KNIME Workflows
- Reading: Evaluation of Decision Tree in Spark
- Vidéo: 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
Noté: Model Evaluation
Noté: Model Evaluation in KNIME and Spark Quiz
SEMAINE 5
Regression, Cluster Analysis, and Association Analysis
8 vidéos, 6 lectures
- Vidéo: Linear Regression
- Reading: Slides: Regression
- Vidéo: Cluster Analysis
- Vidéo: k-Means Clustering
- Reading: Slides: Cluster Analysis
- Discussion Prompt: Clustering Applications
- Vidéo: Association Analysis
- Vidéo: Association Analysis in Detail
- Reading: Slides: Association Analysis
- Discussion Prompt: Applications of Association Analysis
- Vidéo: Machine Learning With Big Data - Final Remarks
- Reading: Description of Minute Weather Dataset
- Reading: Cluster Analysis in Spark
- Vidéo: Cluster Analysis in Spark
- Reading: PDFs of Cluster Analysis in Spark Hands-On Readings
Noté: Regression, Cluster Analysis, & Association Analysis
Noté: Cluster Analysis in Spark Quiz
FAQ
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Créateurs
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
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...