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Machine Learning Algorithms: Supervised Learning Tip to Tail
  • Level Foundation
  • المدة 9 ساعات hours
  • الطبع بواسطة Alberta Machine Intelligence Institute
  • Offered by

عن

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.

الوحدات

Classification in a Nutshell

    • 3 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Supervised Learning Basics

3 Videos

  • Introduction to the Course
  • What does a classifier actually do?
  • Classification in scikit-learn

1 Readings

  • Math Review

Decision Trees

    • 2 Videos
    • 2 Readings
    • 1 Lab
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1 Labs

  • Decision Trees

2 Videos

  • What are decision trees?
  • Generalization and overfitting

2 Readings

  • Scikitlearn documentation for decision trees (Optional)
  • Scikitlearn documentation for random forests (Optional)

k-Nearest Neighbours

    • 3 Videos
    • 1 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Understanding Classification with Decision Trees and k-NN

1 Labs

  • k-NN

3 Videos

  • Classification using k-nearest neighbours
  • Distance measures
  • Weekly summary

1 Readings

  • Scikitlearn documentation for k-nearest neighbours (Optional)

Finding Lines

    • 4 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Regression Basics

4 Videos

  • Line-fitting
  • Optimal line-fitting
  • Loss and Convexity
  • Gradient Descent

1 Readings

  • Scikitlearn documentation for linear regression (Optional)

Simple vs Expressive

    • 3 Videos
    • 1 Assignment
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1 Assignment

  • Understanding Model Complexity

3 Videos

  • Nonlinear features and model complexity
  • Bias and variance tradeoff
  • Regularizers

From Regression to Classification

    • 2 Videos
    • 2 Assignment
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2 Assignment

  • From Regression to Classification
  • The Regression side of Supervised Learning

2 Videos

  • Loss for Classification
  • Weekly summary

Models with Transfer Functions

    • 2 Videos
    • 1 Lab
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1 Labs

  • Logistic Regression

2 Videos

  • Logistic Regression
  • Neural Networks

Support Vector Machines

    • 2 Videos
    • 1 Assignment
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1 Assignment

  • Understanding Support Vector Machines

2 Videos

  • Hinge Loss
  • Basics of Support Vector Machines

Infinite Feature Expansions

    • 2 Videos
    • 1 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Regression-based Classification

1 Labs

  • SVMs and Kernels

2 Videos

  • Kernels
  • Weekly Summary

1 Readings

  • Scikitlearn documentation for SVMs (Optional)

Model Assessment

    • 3 Videos
    • 1 Readings
Show info about module content

3 Videos

  • Regression assessment
  • Classification assessment
  • Learning Curves

1 Readings

  • Some resources on model assessment (Optional)

Testing and Validation Procedures

    • 2 Videos
    • 1 Lab
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1 Labs

  • Splitting the Data

2 Videos

  • Testing your models
  • Cross validation

Parameter Tuning

    • 3 Videos
    • 1 Assignment
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1 Assignment

  • Contrasting Models

3 Videos

  • Parameter tuning and grid search
  • Model Parameters
  • Weekly Summary

Auto Summary

Dive into "Machine Learning Algorithms: Supervised Learning Tip to Tail" to master the essentials of supervised learning in data science and AI. Guided by expert instructors from the Alberta Machine Intelligence Institute, this engaging course covers decision trees, k-nearest neighbours, and support vector machines through practical case studies. Enhance your Python programming, linear algebra, and statistics skills over 540 minutes of in-depth content. Available on Coursera with flexible subscription options, it's ideal for beginners in data science looking to advance their knowledge.

Anna Koop
Instructor

Anna Koop