University of Colorado Boulder - Introduction to Machine Learning: Supervised Learning
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Introduction to Machine Learning: Supervised Learning at Coursera Overview
Duration | 41 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to Machine Learning: Supervised Learning at Coursera Highlights
- Flexible deadlines in accordance to your schedule.
- Earn a Certificate upon completion
Introduction to Machine Learning: Supervised Learning at Coursera Course details
- 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.
- 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.
- This course can be taken for academic credit as part of CU Boulder's Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder's departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics.
Introduction to Machine Learning: Supervised Learning at Coursera Curriculum
Introduction to Machine Learning, Linear Regression
Introduction
Simple Linear Regression
Least Squared Method
Model Fitness and R-squared
Coefficient Significance and Test Error
Welcome and Where to Find Help
Information on Peer Reviews
Course Textbooks
Things of Note for Programming Assignments
Peer Review Guidelines and Expectations
Honor Code Expectations
Module 1 Slides
ISLR 3.1: Simple Linear Regression
ISLR 3.1.1: Estimating the Coefficients
ISLR 3.1.2: Assessing the Accuracy of the Coefficient Estimates
ISLR 3.1.3: Assessing the Accuracy of the Model
Programming Assignments Quiz
Honor Code Expectations
Week 1 Quiz
Multilinear Regression
Linear Regression with Higher-Order Terms: Polynomial Regression
Bias-Variance Trade-Off
Linear Regression with Multiple Features
Feature Selection, Correlation, and Interaction
Module 2 Slides
ISLR 3.2: Multiple Linear Regression
ISLR 3.3.2: Extensions of the Linear Model
ISLR 2.1: What Is Statistical Learning?
ISLR 2.2.2: The Bias-Variance Trade-Off
ISLR 3.3.3: Potential Problems
Week 2 Quiz
Logistic Regression
Logistic Regression Introduction
Logistic Regression Optimization
Performance Metrics in Classification
Sklearn Library Usage and Examples
Module 3 Slides
ISLR 4.1 - 4.3.1: An Overview of Classification - Logistic Regression
ISLR 4.3.2: Estimating the Regression Coefficients
Confusion Matrix
ISLR 6.2.1- 6.2.3 and 5.1: Ridge Regression and Cross-Validation
Logistic Regression
Week 3 Quiz
Non-parametric Models: KNN and Decision Trees
Intro to Non-parametric and K-nearest Neighbors
Decision Tree Intro, Decision Tree Regressor
Decision Tree Classifier, Metrics (Gini and Entropy)
Sklearn Usage, DT Hyperparameters and Early Stopping
Minimal Cost-complexity Pruning
Module 4 Slides
ISLR: K-Nearest Neighbors
ISLR 8.1.1: The Basics of Decision Trees-Regression Trees
ISLR 8.1.2: Classification Trees
Decision Tree Classifier
ISLR: Tree Pruning
Week 4 Quiz
Ensemble Methods
Ensemble Method Intro: Random Forest
Boosting Introduction
AdaBoost Algorithm
Gradient Boosting
Module 5 Slides
ISLR 8.2.1, 8.2.2: Bagging and Random Forests
ISLR 8.2.3: Boosting
ESLII 10.1 - 10.4: Boosting Methods - Exponential Loss and AdaBoost
ESLII 10.10, 10.11: Gradient Boosting
Week 5 Quiz
Kernel Method
Support Vector Machine Introduction
Support Vector Machine: Soft Margin Classifier
Support Vector Machine: Kernel Trick
Support Vector Machine: Performance
Module 6 Slides
ISLR 9.1: Maximal Margin Classifier
ISLR 9.2: Support Vector Classifiers
ISLR 9.3: Support Vector Machines
Week 6 Quiz
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