Machine Learning With Big Data
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Machine Learning With Big Data
This course is part of Big Data Specialization
Instructors: Mai Nguyen
79,300 already enrolled
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2,505 reviews
2,505 reviews
Skills you'll gain
- Model Evaluation
- Exploratory Data Analysis
- Data Transformation
- Descriptive Statistics
- Big Data
- Machine Learning Algorithms
- Data Mining
- Data Preprocessing
- Machine Learning
- Statistical Analysis
- Data Wrangling
- Machine Learning Methods
- Applied Machine Learning
- Model Training
- Data Analysis
- Unsupervised Learning
- Machine Learning Software
- Regression Analysis
Tools you'll learn
Details to know
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There are 7 modules in this course
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
What's included
2 videos2 discussion prompts
2 videos•Total 14 minutes
- Welcome to Machine Learning With Big Data•4 minutes
- Summary of Big Data Integration and Processing•11 minutes
2 discussion prompts•Total 20 minutes
- Getting to Know You: Tell us about yourself and why you are taking this course.•10 minutes
- Discussion Forum for Course Content Issues•10 minutes
What's included
7 videos6 readings1 assignment1 discussion prompt
7 videos•Total 45 minutes
- Machine Learning Overview•8 minutes
- Categories Of Machine Learning Techniques•8 minutes
- Machine Learning Process•3 minutes
- Goals and Activities in the Machine Learning Process•11 minutes
- CRISP-DM•5 minutes
- Scaling Up Machine Learning Algorithms•5 minutes
- Tools Used in this Course•5 minutes
6 readings•Total 125 minutes
- Slides: Machine Learning Overview and Applications•25 minutes
- Downloading and Installing Docker Desktop Instructions•10 minutes
- Instroduction to Jupyter Notebooks•10 minutes
- Downloading Hands-On Materials•10 minutes
- Basic terminal shell commands•10 minutes
- Downloading, Installing and Using KNIME•60 minutes
1 assignment•Total 20 minutes
- Machine Learning Overview•20 minutes
1 discussion prompt•Total 10 minutes
- Machine Learning in Everyday Life•10 minutes
What's included
6 videos4 readings2 assignments1 discussion prompt
6 videos•Total 39 minutes
- Data Terminology•5 minutes
- Data Exploration•4 minutes
- Data Exploration through Summary Statistics•8 minutes
- Data Exploration through Plots•8 minutes
- Exploring Data with KNIME Plots•9 minutes
- Data Exploration in Spark•5 minutes
4 readings•Total 70 minutes
- Slides: Data Exploration Overview and Terminology•10 minutes
- Description of Daily Weather Dataset•10 minutes
- Exploring Data with KNIME Plots•40 minutes
- Data Exploration in Spark•10 minutes
2 assignments•Total 40 minutes
- Data Exploration•20 minutes
- Data Exploration in KNIME and Spark Quiz•20 minutes
1 discussion prompt•Total 10 minutes
- What's Wrong with Pie Charts?•10 minutes
What's included
8 videos3 readings2 assignments2 discussion prompts
8 videos•Total 40 minutes
- Data Preparation•3 minutes
- Data Quality•4 minutes
- Addressing Data Quality Issues•5 minutes
- Feature Selection•5 minutes
- Feature Transformation•5 minutes
- Dimensionality Reduction•7 minutes
- Handling Missing Values in KNIME•5 minutes
- Handling Missing Values in Spark•4 minutes
3 readings•Total 60 minutes
- Slides: Data Preparation for Machine Learning•30 minutes
- Handling Missing Values in KNIME•20 minutes
- Handling Missing Values in Spark•10 minutes
2 assignments•Total 45 minutes
- Data Preparation•25 minutes
- Handling Missing Values in KNIME and Spark Quiz•20 minutes
2 discussion prompts•Total 20 minutes
- Quality Issues with Real Data•10 minutes
- Domain Knowledge in Data Preparation•10 minutes
What's included
8 videos5 readings2 assignments1 discussion prompt
8 videos•Total 60 minutes
- Classification•4 minutes
- Building and Applying a Classification Model•6 minutes
- Classification Algorithms•3 minutes
- k-Nearest Neighbors•5 minutes
- Decision Trees•13 minutes
- Naïve Bayes•14 minutes
- Classification using Decision Tree in KNIME•8 minutes
- Classification in Spark•7 minutes
5 readings•Total 130 minutes
- Slides: What is Classification?•10 minutes
- Slides: Classification Algorithms•10 minutes
- Classification using Decision Tree in KNIME•45 minutes
- Interpreting a Decision Tree in KNIME•20 minutes
- Classification in Spark•45 minutes
2 assignments•Total 36 minutes
- Classification•20 minutes
- Classification in KNIME and Spark Quiz•16 minutes
1 discussion prompt•Total 10 minutes
- Why Exclude Relative Humidity?•10 minutes
What's included
7 videos6 readings2 assignments1 discussion prompt
7 videos•Total 44 minutes
- Generalization and Overfitting•5 minutes
- Overfitting in Decision Trees•4 minutes
- Using a Validation Set•10 minutes
- Metrics to Evaluate Model Performance•10 minutes
- Confusion Matrix•7 minutes
- Evaluation of Decision Tree in KNIME•5 minutes
- Evaluation of Decision Tree in Spark•3 minutes
6 readings•Total 90 minutes
- Slides: Overfitting: What is it and how would you prevent it?•10 minutes
- Slides: Model evaluation metrics and methods•10 minutes
- Evaluation of Decision Tree in KNIME•30 minutes
- Completed KNIME Workflows•10 minutes
- Evaluation of Decision Tree in Spark•20 minutes
- Comparing Classification Results for KNIME and Spark•10 minutes
2 assignments•Total 50 minutes
- Model Evaluation•20 minutes
- Model Evaluation in KNIME and Spark Quiz•30 minutes
1 discussion prompt•Total 10 minutes
- Model Interpretability vs. Accuracy•10 minutes
What's included
8 videos5 readings2 assignments2 discussion prompts
8 videos•Total 48 minutes
- Regression Overview•4 minutes
- Linear Regression•4 minutes
- Cluster Analysis•8 minutes
- k-Means Clustering•8 minutes
- Association Analysis•5 minutes
- Association Analysis in Detail•9 minutes
- Machine Learning With Big Data - Final Remarks•1 minute
- Cluster Analysis in Spark•8 minutes
5 readings•Total 100 minutes
- Slides: Regression•10 minutes
- Slides: Cluster Analysis•10 minutes
- Slides: Association Analysis•10 minutes
- Description of Minute Weather Dataset•10 minutes
- Cluster Analysis in Spark•60 minutes
2 assignments•Total 50 minutes
- Regression, Cluster Analysis, & Association Analysis•30 minutes
- Cluster Analysis in Spark Quiz•20 minutes
2 discussion prompts•Total 20 minutes
- Clustering Applications•10 minutes
- Applications of Association Analysis•10 minutes
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Reviewed on Oct 30, 2018
Reasonable overview. The VM environment is a major challenge for my hardware. Takes more time to make it work than it should. I am wondering if a cloud solution e.g. GCP would be better.
Reviewed on Dec 26, 2019
The fact that the assignments are graded means that there’s incentive to work on them, solve problems, and ask questions. Traditional online courses don’t offer that incentive.
Reviewed on Jul 30, 2020
I am so much pleased with this course. Very much convinced by the presentation, way of speech, and the script of the Instructor. I am so excited to learn more about Machine Learning.
Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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