Course Summary
This course teaches the fundamentals of machine learning and data analysis. It covers topics such as linear regression, classification, clustering, and neural networks, and provides hands-on experience with real-world datasets.Key Learning Points
- Learn the basics of machine learning and data analysis
- Gain hands-on experience with real-world datasets
- Understand linear regression, classification, clustering, and neural networks
Job Positions & Salaries of people who have taken this course might have
- USA: $62,453
- India: ₹4,69,237
- Spain: €36,310
- USA: $62,453
- India: ₹4,69,237
- Spain: €36,310
- USA: $120,931
- India: ₹15,00,000
- Spain: €50,000
- USA: $62,453
- India: ₹4,69,237
- Spain: €36,310
- USA: $120,931
- India: ₹15,00,000
- Spain: €50,000
- USA: $113,309
- India: ₹12,00,000
- Spain: €38,000
Related Topics for further study
Learning Outcomes
- Understand the fundamentals of machine learning and data analysis
- Gain hands-on experience with real-world datasets
- Build and evaluate machine learning models
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming in Python
- Familiarity with linear algebra and statistics
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Hands-on projects
Similar Courses
- Applied Data Science with Python
- Machine Learning
- Data Mining
Related Education Paths
Notable People in This Field
- Andrew Ng
- Yann LeCun
- Fei-Fei Li
Related Books
Description
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to:
Outline
- What Does Good Data look like?
- Introduction to the Course
- Business Understanding and Problem Discovery
- No Free Lunch Theorem
- Exploring the process of problem definition
- Data Acquisition and Understanding
- Metadata Matters
- Dealing with Multimodal Data
- Features and transformations of raw data
- Identifying Data from Problem
- Case Study: Problem from Data
- Weekly Summary What does good data look like?
- Machine Learning Process Lifecycle Review
- Match Data to the needs of the learning Algorithm
- Business Understanding and Problem Discovery (BUPD) Review
- Data Acquisition and Understanding Review
- Module 1 Quiz
- Preparing your Data for Machine Learning Success
- Data Warehousing
- Converting to Useful Forms
- Data Quality
- How Much Data Do I Need?
- Everything has to be Numbers
- Types of Data
- Aligning Similar Data
- Imputing Missing Values
- Data Transformations
- Weekly Summary: Preparing your Data for Machine Learning Success
- Data Cleaning: Everybody's favourite task
- Data Warehousing Review
- Everything has to be Numbers Review
- Types of Data Review
- Module 2 Quiz
- Feature Engineering for MORE Fun & Profit
- What are the simplest Features to try
- Useful/Useless Features
- How Many Features?
- What is Unsupervised Learning
- Feature Selection
- Feature Extraction
- Transfer Learning
- Weekly Summary: Feature Engineering for MORE Fun & Profit
- Possibilities for Text Features
- Word Embeddings
- Understanding Features
- Building Good Features
- Understanding Transfer Learning
- Bad Data
- Imbalanced Data
- Generalization and how machines actually learn
- Bias in Data Sources
- Bias and variance tradeoff
- Outliers
- Skewed Distributions
- Badness Multipliers
- Live Data Danger
- Weekly Summary: Bad Data
- Mistakes Computers Make
- Data: Skewed Distributions
- Live Data Dangers
- Module 4 Quiz
Summary of User Reviews
Discover the world of data and machine learning with this comprehensive course from Coursera. Users have praised the course for its in-depth content and practical approach. With an overall positive rating from many users, this course is definitely worth checking out.Key Aspect Users Liked About This Course
in-depth contentPros from User Reviews
- Practical approach to learning
- Many real-world examples
- Great for beginners
- Good pacing
- Engaging lectures
Cons from User Reviews
- Some technical issues reported
- Not suitable for advanced learners
- Lack of interaction with instructors
- Limited scope in certain areas
- Requires a strong foundation in math
Anna Koop
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