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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
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
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
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
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