[ AI ] Coursera : Machine Learning with Python Week 1

이주 weekwith.me·2022년 6월 21일
0

AI

목록 보기
1/3
post-thumbnail

블로그를 이전 중이라 완료되기 전까지는 벨로그에 작성할 계획입니다.
이후 모든 글은 https://weekwith.me 에 작성 예정이니 다른 글이 궁금하시다면 해당 링크를 통해 방문해주세요.

또한 본 글의 모든 내용은 Coursera IBM AI Engineering Professional Certificate 과정의 강의를 참고했음을 알립니다.

Introduction to Machine Learning

Definition

  • Machine Learning is the subfield of the computer science that gives "computers the ability to learn without being explicitly programmed."

Major techniques

Regression / Estimation

  • Predicting continuous values

Classification

  • Predicting the item class/category of a case

Clustering

  • Finding the structure of data; summarization

Associations

  • Associating frequent co-occurring items/events

Anomaly detection

  • Discovering abnormal and unusual cases

Sequence mining

  • Predicting next events; click-stream (Markov Model, HMM)

Dimension Reduction

  • Reducing the size of data (PCA)

Recommendation systems

  • Recommending items

Difference between artifical intelligence, machine learning, and deep learning

AI components

  • Computer Vision
  • Language Processing
  • Creativity

Machine learning

  • Classification
  • Clustering
  • Neural Network

Revolution in ML

  • Deep learning

Section Quiz

Question 1

  • Which Machine Learning technique is proper for grouping of similar cases in a dataset, for example to find similar patients, or for customers segmentation in a bank?

Answer

  • Clustering

Python for Machine Learning

Section Quiz

Question 1

  • Why Scikit is a proper library for Machine Learning?

Answer

  • Scikit-learn is a free machine learning library that works with Numpy and Scipy.
  • Scikit-learn has most of machine learning algorithms.

Supervised vs Unsupervised

Supervised

  • Teach the model, then with that knowledge, it can predict unknown or future instances.

Types of supervised learning

Classification

  • Classification is the process of predicting discrete class labels or categories.

Regression

  • Regression is the process of predicting continuous values.

Unsupervised

  • The model works on its own to discover information.

Types of unsupervised learning

Dimension reduction

Density estimation

Market basket analysis

Clustering

  • Clustering is grouping of data points or objects that are somehow similar by those things.
    • Discovering structure
    • Summarization
    • Anomaly detection

Summarization

Supervised Learning

  • Classification: Classifies labeled data
  • Regression: Predicts trends using previous labeled data
  • Has more evaluation methods than unsupervised learning
  • Controlled environment

Unsupervised Learning

  • Clustering: Finds patterns and groupings from unlabeled data
  • Has fewer evaluation methods than supervised learning
  • Less controlled environment

Section Quiz

Question 1

  • Which techniques are considered as Supervised learning?

Answer

  • Regression
  • Classification

Quiz

Question 1

  • Supervised learning with unlabeled data, while unsupervised learning deals with labeled data.

Answer

  • False

Question 2

  • The "Regression" technique in Machine Learning is a group of algorithms that are used for

Answer

  • Predicting a continous value; for example predicting the price of a house based on its characteristics.

Question 3

  • When comparing Supervised with Unsupervised learning, is this sentence True or False?
    • contrast to Supervised learning, Unsupervised learning has more models and more evaluation methods that can be used in order to ensure the outcome of the model is accurate.

Answer

  • False
profile
Be Happy 😆

0개의 댓글