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.
Enseigné par : Mai Nguyen, Lead for Data Analytics
San Diego Supercomputer CenterEnseigné 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 |
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
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| Recevoir une Note Finale | Disponible |
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It's a very good introductory course, in this course we acquire the first notions on clustering, association, classification and regression.
Great!
My name is Jose Antonio. I am looking for a new Data Scientist career (https://www.linkedin.com/in/joseantonio11)
I did this specialization to get new knowledge about Machine Learning and better understand the technology and your practical applications.
The course was excellent and the classes well taught by teachers.
Congratulations to Coursera team and Instructors.
Regards.
Jose Antonio.
very good topic learnt from you
A very good introduction to fundamentals of machine learning from a practitioner's perspective, without going into mathematical details (there are many other courses for that). The focus was on using big data systems for performing ML tasks and it was introduced quite well.