Course Summary
This course on Coursera provides an introduction to machine learning and data analysis. Students will learn about different machine learning algorithms and techniques, and gain hands-on experience working with real-world datasets.Key Learning Points
- Gain practical experience working with real-world datasets
- Learn about different machine learning algorithms and techniques
- Develop a strong foundation in data analysis and statistics
Job Positions & Salaries of people who have taken this course might have
- USA: $113,000
- India: ₹1,205,000
- Spain: €44,000
- USA: $113,000
- India: ₹1,205,000
- Spain: €44,000
- USA: $112,000
- India: ₹1,050,000
- Spain: €38,000
- USA: $113,000
- India: ₹1,205,000
- Spain: €44,000
- USA: $112,000
- India: ₹1,050,000
- Spain: €38,000
- USA: $62,000
- India: ₹582,000
- Spain: €21,000
Related Topics for further study
Learning Outcomes
- Develop a strong understanding of machine learning and data analysis
- Gain practical experience working with real-world datasets
- Learn how to use Python to analyze data and build models
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics and programming
- Familiarity with Python is recommended
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Data Science with Python
- Data Science Essentials
- Data Science Methodology
Related Education Paths
Notable People in This Field
- Andrew Ng
- Kaggle
Related Books
Description
Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering. By completing this course, you will learn how to apply, test, and interpret machine learning algorithms as alternative methods for addressing your research questions.
Outline
- Decision Trees
- What Is Machine Learning?
- Machine Learning and the Bias Variance Trade-Off
- What Is a Decision Tree?
- What is the Process of Growing a Decision Tree?
- Building a Decision Tree with SAS
- Strengths and Weaknesses of Decision Trees in SAS
- Building a Decision Tree with Python
- Some Guidance for Learners New to the Specialization
- SAS or Python - Which to Choose?
- Getting Started with SAS
- Getting Started with Python
- Course Codebooks
- Course Data Sets
- Uploading Your Own Data to SAS
- Data Set for Decision Tree Videos (tree_addhealth.csv)
- SAS Code: Decision Trees
- CART Paper - Prevention Science
- Python Code: Decision Trees
- Installing Graphviz and pydotplus
- Getting Set up for Assignments
- Tumblr Instructions
- Assignment Example
- Random Forests
- What Is A Random Forest and How Is It "Grown"?
- Building a Random Forest with SAS
- Building a Random Forest with Python
- Validation and Cross-Validation
- SAS code: Random Forests
- The HPForest Procedure in SAS
- Python Code: Random Forests
- Assignment Example
- Lasso Regression
- What is Lasso Regression?
- Testing a Lasso Regression with SAS
- Data Management for Lasso Regression in Python
- Testing a Lasso Regression Model in Python
- Lasso Regression Limitations
- SAS Code: Lasso Regression
- Python Code: Lasso Regression
- Assignment Example
- K-Means Cluster Analysis
- What Is a k-Means Cluster Analysis?
- Running a k-Means Cluster Analysis in SAS, pt. 1
- Running a k-Means Cluster Analysis in SAS, pt. 2
- Running a k-Means Cluster Analysis in Python, pt. 1
- Running a k-Means Cluster Analysis in Python, pt. 2
- k-Means Cluster Analysis Limitations
- SAS Code: k-Means Cluster Analysis
- Python Code: k-Means Cluster Analysis
- Assignment Example
Summary of User Reviews
Read reviews of the Machine Learning and Data Analysis course on Coursera. Users highly recommend this course for its comprehensive and practical approach to machine learning. Many users found the hands-on projects to be the best part of the course.Pros from User Reviews
- Comprehensive and practical approach to machine learning
- Hands-on projects provide real-world experience
- Great instructor who explains complex concepts clearly
Cons from User Reviews
- Some users found the pace of the course to be too fast
- Course materials could be more organized
- Not enough focus on specific programming languages or tools
Jen Rose
4.2 Raiting
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