Machine Learning Algorithms: Supervised Learning Tip to Tail
5.0 /5- Offered byCoursera
- Public/Government Institute
Machine Learning Algorithms: Supervised Learning Tip to Tail at Coursera Overview
Duration | 9 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Machine Learning Algorithms: Supervised Learning Tip to Tail at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 4 in the Machine Learning: Algorithms in the Real World Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Approx. 9 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Machine Learning Algorithms: Supervised Learning Tip to Tail at Coursera Course details
- 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.
- To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode).
- This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
Machine Learning Algorithms: Supervised Learning Tip to Tail at Coursera Curriculum
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)
Supervised Learning Basics
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)
Regression Basics
Understanding Model Complexity
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)
Understanding Support Vector Machines
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)
Contrasting Models
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Machine Learning Algorithms: Supervised Learning Tip to Tail at Coursera Students Ratings & Reviews
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