Data Science
30
Introduction to Machine Learning
Home
1
Introduction
2
Schedule
R Programming
3
Installation
4
Introduction to R
5
Getting Started
6
IDEs
7
R packages
8
Basic operations
9
Data Types & Vectors
10
Data Structures
11
Indexing
12
Factors
13
Base Data I/O
14
Read and Write Excel Data
15
Vectorized Operations
16
Control Flow
17
Summarizing Data
18
Aggregate
19
Writing Functions
20
Function Scoping
21
The Apply Family
22
Base Graphics
Data Science
23
Terminal
24
Version Control with Git and GitHub
25
Demo Dataset
26
Probability
27
Statistics
28
Hypothesis Testing
29
Data Visualization
30
Introduction to Machine Learning
31
Clustering
32
Decomposition and Dimensionality Reduction
33
Supervised Learning
34
AI Assistants for Data Science
35
References
Table of contents
30.1
Learning objectives
30.2
From hypothesis testing to prediction
30.3
Samples, resamples, and generalization
30.4
Supervised and unsupervised learning
30.5
Classification and regression
30.6
Core algorithm families
30.6.1
Linear models and regularization
30.6.2
Decision trees and ensembles
30.6.3
Deep learning
30.7
Clustering and decomposition
30.8
Hands-on practice
30.9
Checkpoint
30.10
Further study
Data Science
30
Introduction to Machine Learning
30
Introduction to Machine Learning
30.1
Learning objectives
Distinguish supervised and unsupervised learning.
Identify classification and regression questions.
Explain why predictive performance must be assessed on unseen observations.
30.2
From hypothesis testing to prediction
30.3
Samples, resamples, and generalization
30.4
Supervised and unsupervised learning
30.5
Classification and regression
30.6
Core algorithm families
30.6.1
Linear models and regularization
30.6.2
Decision trees and ensembles
30.6.3
Deep learning
30.7
Clustering and decomposition
30.8
Hands-on practice
30.9
Checkpoint
30.10
Further study
29
Data Visualization
31
Clustering