Machine Learning Algorithms: Supervised Learning Tip to Tail
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Approx. 9 hours to complete
Course Summary
Learn about machine learning classification algorithms and their practical applications in this comprehensive course. Gain hands-on experience with popular algorithms and techniques such as decision trees, logistic regression, and k-nearest neighbors.Key Learning Points
- Explore various classification algorithms and learn how to implement them in real-world scenarios.
- Understand the strengths and weaknesses of different algorithms and how to choose the right one for a particular problem.
- Develop practical skills by working on hands-on exercises and projects throughout the course.
Job Positions & Salaries of people who have taken this course might have
- Machine Learning Engineer
- USA: $112,000
- India: ₹1,000,000
- Spain: €35,000
- Data Scientist
- USA: $116,000
- India: ₹1,200,000
- Spain: €40,000
- AI Researcher
- USA: $128,000
- India: ₹1,500,000
- Spain: €50,000
Related Topics for further study
- Classification Algorithms
- Decision Trees
- Logistic Regression
- K-Nearest Neighbors
- Real-World Applications
Learning Outcomes
- Understand the fundamentals of machine learning classification algorithms.
- Gain practical skills by working on hands-on exercises and projects.
- Learn how to choose the right algorithm for a particular problem.
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming concepts and statistics.
- Familiarity with machine learning concepts is helpful but not required.
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Hands-on
Similar Courses
- Introduction to Machine Learning
- Applied Data Science with Python
Related Education Paths
Notable People in This Field
- Andrew Ng
- Yann LeCun
Related Books
Description
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML.
Outline
- Classification using Decision Trees and k-NN
- Introduction to the Course
- What does a classifier actually do?
- Classification in scikit-learn
- What are decision trees?
- Generalization and overfitting
- Classification using k-nearest neighbours
- Distance measures
- Weekly summary
- Math Review
- Scikitlearn documentation for decision trees (Optional)
- Scikitlearn documentation for random forests (Optional)
- Scikitlearn documentation for k-nearest neighbours (Optional)
- Understanding Classification with Decision Trees and k-NN
- Functions for Fun and Profit
- Line-fitting
- Optimal line-fitting
- Loss and Convexity
- Gradient Descent
- Nonlinear features and model complexity
- Bias and variance tradeoff
- Regularizers
- Loss for Classification
- Weekly summary
- Scikitlearn documentation for linear regression (Optional)
- From Regression to Classification
- The Regression side of Supervised Learning
- Regression for Classification: Support Vector Machines
- Logistic Regression
- Neural Networks
- Hinge Loss
- Basics of Support Vector Machines
- Kernels
- Weekly Summary
- Scikitlearn documentation for SVMs (Optional)
- Regression-based Classification
- Contrasting Models
- Regression assessment
- Classification assessment
- Learning Curves
- Testing your models
- Cross validation
- Parameter tuning and grid search
- Model Parameters
- Weekly Summary
- Some resources on model assessment (Optional)
Summary of User Reviews
Find out what students are saying about the Machine Learning Classification Algorithms course on Coursera. This course has received positive reviews and is highly recommended for those interested in learning about classification algorithms. Many users found the course to be easy to understand and the instructor to be engaging and informative.Key Aspect Users Liked About This Course
The course is easy to understand and the instructor is engaging and informative.Pros from User Reviews
- The course is well-structured and easy to follow
- The instructor is knowledgeable and engaging
- The course covers a wide range of topics
- The assignments are challenging and help reinforce the concepts
- The course is a great introduction to machine learning
Cons from User Reviews
- The course can be challenging for beginners with no programming experience
- Some users found the pace of the course to be too fast
- The course does not cover some advanced topics
- The peer review process can be time-consuming and frustrating
- The course does not provide as much hands-on experience as some users would like
Anna Koop
4.7 Raiting
Machine Learning Algorithms: Supervised Learning Tip to Tail
- 4.7
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This course takes you from understanding the fundamentals of a machine learning project. To be successful, you should have at least beginner-level background in Python programming (e....
