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Introduction to Machine Learning: Supervised Learning
  • Level Professional
  • المدة 40 ساعات hours
  • الطبع بواسطة University of Colorado Boulder
  • Offered by

عن

In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. In this course, you will need to have a solid foundation in Python or sufficient previous experience coding with other programming languages to pick up Python quickly. We will be learning how to use data science libraries like NumPy, pandas, matplotlib, statsmodels, and sklearn. The course is designed for programmers beginning to work with those libraries. Prior experience with those libraries would be helpful but not necessary. College-level math skills, including Calculus and Linear Algebra, are required. Our hope for this course is that the math will be understandable but not intimidating. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

الوحدات

Introduction

    • 1 Videos
    • 3 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Introduce Yourself

1 Videos

  • Introduction

3 Readings

  • Earn Academic Credit for Your Work!
  • Course Support
  • Course Textbooks

Programming Assignments

    • 1 Readings
    • 1 Quiz
Show info about module content

1 Readings

  • Things of Note for Programming Assignments

1 Quiz

  • Programming Assignments Quiz

Peer Review and Honor Code Expectations

    • 3 Readings
    • 1 Discussion
    • 1 Quiz
    • 1 Assignment
Show info about module content

1 Assignment

  • Peer Review Expectations

1 Discussions

  • Peer Review Expectations

3 Readings

  • Peer Review Guidelines and Expectations
  • A Note About Peer Review Resubmissions
  • Honor Code Expectations

1 Quiz

  • Honor Code Expectations

Linear Regression Model Introduction

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Simple Linear Regression

1 Readings

  • ISLR 3.1: Simple Linear Regression

Linear Regression Model Optimization

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Least Squared Method

1 Readings

  • ISLR 3.1.1: Estimating the Coefficients

Linear Regression Model Evaluation

    • 2 Videos
    • 2 Readings
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2 Videos

  • Model Fitness and R-squared
  • Coefficient Significance and Test Error

2 Readings

  • ISLR 3.1.2: Assessing the Accuracy of the Coefficient Estimates
  • ISLR 3.1.3: Assessing the Accuracy of the Model

Slide Decks

    • 1 Readings
Show info about module content

1 Readings

  • Module 1 Slides

Assessments

    • 1 Quiz
    • 1 PeerReview
    • 1 Programming
Show info about module content

1 Programming

  • Week 1: Data Cleaning and EDA

1 Peer Review

  • Week 1: Data Cleaning and EDA

1 Quiz

  • Week 1 Quiz

Introduction to Multilinear Regression: Polynomial Regression Type

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Linear Regression with Higher-Order Terms: Polynomial Regression

2 Readings

  • ISLR 3.2: Multiple Linear Regression
  • ISLR 3.3.2: Extensions of the Linear Model

Bias-Variance Trade-Off

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Bias-Variance Trade-Off

1 Readings

  • ISLR 2.2.2: The Bias-Variance Trade-Off

Multiple Linear Regression Model Optimization

    • 2 Videos
    • 1 Readings
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2 Videos

  • Linear Regression with Multiple Features
  • Feature Selection, Correlation, and Interaction

1 Readings

  • ISLR 3.3.3: Potential Problems

Slide Decks

    • 1 Readings
Show info about module content

1 Readings

  • Module 2 Slides

Assessments

    • 1 Quiz
    • 1 PeerReview
    • 1 Programming
Show info about module content

1 Programming

  • Week 2: Multiple Linear Regression

1 Peer Review

  • Week 2: Multiple Linear Regression

1 Quiz

  • Week 2 Quiz

Introduction to Logistic Regression

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Logistic Regression Introduction

1 Readings

  • ISLR 4.1 - 4.3.1: An Overview of Classification - Logistic Regression

Logistic Regression Model Optimization

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Logistic Regression Optimization

1 Readings

  • ISLR 4.3.2: Estimating the Regression Coefficients

Evaluating Logistic Regression Models

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Performance Metrics in Classification

2 Readings

  • Confusion Matrix
  • ISLR 6.2.1- 6.2.3 and 5.1: Ridge Regression and Cross-Validation

Coding Examples: Sklearn Library

    • 1 Videos
    • 1 Readings
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1 Videos

  • Sklearn Library Usage and Examples

1 Readings

  • Logistic Regression

Slide Decks

    • 1 Readings
Show info about module content

1 Readings

  • Module 3 Slides

Assessments

    • 1 Quiz
    • 1 PeerReview
    • 1 Programming
Show info about module content

1 Programming

  • Week 3: Logistic Regression

1 Peer Review

  • Week 3: Logistic Regression

1 Quiz

  • Week 3 Quiz

Introduction to KNN Model

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Intro to Non-parametric and K-nearest Neighbors

1 Readings

  • ISLR: K-Nearest Neighbors

Introduction to Decision Tree Model

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Decision Tree Intro, Decision Tree Regressor

1 Readings

  • ISLR 8.1.1: The Basics of Decision Trees-Regression Trees

Decision Tree Metrics

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Decision Tree Classifier, Metrics (Gini and Entropy)

2 Readings

  • ISLR 8.1.2: Classification Trees
  • Decision Tree Classifier

Optimizing Decision Tree Models

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • Sklearn Usage, DT Hyperparameters and Early Stopping
  • Minimal Cost-complexity Pruning

1 Readings

  • ISLR: Tree Pruning

Slide Decks

    • 1 Readings
Show info about module content

1 Readings

  • Module 4 Slides

Assessments

    • 1 Quiz
    • 1 PeerReview
    • 1 Programming
Show info about module content

1 Programming

  • Week 4: Non-parametric Models

1 Peer Review

  • Week 4: Non-parametric Models

1 Quiz

  • Week 4 Quiz

Introduction to Ensembles: Bagging

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Ensemble Method Intro: Random Forest

1 Readings

  • ISLR 8.2.1, 8.2.2: Bagging and Random Forests

Introduction to Boosting Ensembles

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Boosting Introduction

1 Readings

  • ISLR 8.2.3: Boosting

Boosting Algorithms

    • 2 Videos
    • 2 Readings
Show info about module content

2 Videos

  • AdaBoost Algorithm
  • Gradient Boosting

2 Readings

  • ESLII 10.1 - 10.4: Boosting Methods - Exponential Loss and AdaBoost
  • ESLII 10.10, 10.11: Gradient Boosting

Slide Decks

    • 1 Readings
Show info about module content

1 Readings

  • Module 5 Slides

Assessments

    • 1 Quiz
    • 1 PeerReview
    • 1 Programming
Show info about module content

1 Programming

  • Week 5: Ensembles

1 Peer Review

  • Week 5: Ensembles

1 Quiz

  • Week 5 Quiz

Introduction to Support Vector Machine Model

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Support Vector Machine Introduction

1 Readings

  • ISLR 9.1: Maximal Margin Classifier

Optimizing SVM

    • 2 Videos
    • 2 Readings
Show info about module content

2 Videos

  • Support Vector Machine: Soft Margin Classifier
  • Support Vector Machine: Kernel Trick

2 Readings

  • ISLR 9.2: Support Vector Classifiers
  • ISLR 9.3: Support Vector Machines

SVM Performance

    • 1 Videos
Show info about module content

1 Videos

  • Support Vector Machine: Performance

Slide Decks

    • 1 Readings
Show info about module content

1 Readings

  • Module 6 Slides

Assessments

    • 1 Quiz
    • 1 PeerReview
    • 1 Programming
Show info about module content

1 Programming

  • Week 6: SVM Lab

1 Peer Review

  • Week 6: SVM Lab

1 Quiz

  • Week 6 Quiz

Auto Summary

Explore the dynamic world of supervised machine learning with the "Introduction to Machine Learning: Supervised Learning" course, designed for those passionate about Data Science and AI. Guided by industry professionals from Coursera, this course immerses you in the essentials of machine learning algorithms and prediction tasks, emphasizing when and how to apply models like linear and logistic regression, KNN, Decision Trees, Random Forest, Boosting, and SVM. Ideal for learners with a coding background, the course leverages Python, requiring either prior knowledge or the ability to quickly adapt to the language. You’ll also delve into powerful data science libraries such as NumPy, pandas, matplotlib, statsmodels, and sklearn, making it perfect for programmers new to these tools. Understanding college-level Calculus and Linear Algebra is crucial, as the course aims to demystify the math behind machine learning without making it overwhelming. Whether you're a recent graduate or a working professional, this course offers an excellent opportunity to earn academic credit towards CU Boulder’s MS in Data Science or MS in Computer Science degrees available on Coursera. These accredited graduate programs provide a flexible, pay-as-you-go option with short 8-week sessions. With a course duration of 2400 minutes and a choice between Starter and Professional subscription plans, this professional-level course promises a comprehensive education in supervised learning. Join us and start your journey towards mastering machine learning and enhancing your data science skill set.

Geena Kim
Instructor

Geena Kim