Chapter 1 Flashcards
What is Machine Learning?
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. It means rogramming computers so they can learn from data. Learning means getting better at some task, given some performance measure.
Data Mining
Applying ML techniques to dig into large amounts of data can help discover patterns that were not immediately apparent. This is called data mining.
Four types of problems where ML shines?
ML is great for problems with no algorithmic solutions, to replace long list of hard wired rules, to build systems that adapt to fluctuating environments, and getting insights in large amounts of data(data mining).
NLP
Natural Language Processing. Text classification.
Supervised learning
In supervised learning, the training set you feed to the algorithm includes the desired solutions, called labels.
It has two types : Classification(spam filters), Regression( to predict a target numeric value, such as the price of a car, given a set of features (mileage, age, brand, etc.) called predictors).
Name some supervised learning algorithms.
k-Nearest Neighbors
Linear Regression
Logistic Regression
Support Vector Machines (SVMs)
Decision Trees and Random Forests
Neural networks
Unsupervised learning
In unsupervised learning, the training data is unlabeled.
Can you name four common unsupervised tasks?
Clustering, Visualization and dimensionality reduction
Dimensionality reduction
Simplify the data without losing too much information. One way to do this is to merge several correlated features into one.
Name one Dimensionality reduction technique
Feature extraction.
For example, a car’s mileage may be strongly correlated with its age, so the dimensionality reduction algorithm will merge them into one feature that represents the car’s wear and tear. This is called feature extraction.
Reinforcement Learning
In Reinforcement Learning, the learning system, called an agent, can observe the environment, select and perform actions, and get rewards in return (or penalties in the form of negative rewards). It must then learn by itself what is the best strategy, called a policy, to get the most reward over time. A policy defines what action the agent should choose when it is in a given situation. example teaching robots to walk.
Batch learning
In batch learning, the system is incapable of learning incrementally: it must be trained using all the available data. This will generally take a lot of time and computing resources, so it is typically done offline. First the system is trained, and then it is launched into production and runs without learning anymore; it just applies what it has learned. This is called offline learning.
Online learning
In online learning, you train the system incrementally by feeding it data instances sequentially, either individually or in small groups called mini-batches, as opposed to a batch learning system. This makes it capable of adapting rapidly to both changing data and autonomous systems, and of training on very large quantities of data.
What is out-of-core learning?
Online learning algorithms can be used to train systems on huge datasets that cannot fit in one machine’s main memory. The algorithm loads part of the data, runs a training step on that data, and repeats the process until it has run on all of the data. This is called out-of-core learning.
Whai is learning rate?
It is a parameter of online learning systems of how fast they should adapt to changing data.
