crncck / Machine-Learning

Machine learning algorithms applications with Python & R

Machine Learning

Machine learning is the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.

Machine learning is used in various applications like – voice search technology, image recognition, automated translation, self-driven cars, etc.

1. Basic Concepts in Machine Learning

1.1. Types of Data

graph
A[Types of data] --> B[Quantitative]
A --> C[Qualitative]
C --> D[Nominal]
C --> E[Ordinal]
B --> F[Discrete]
B --> G[Continuous]

• Quantitative data is data that can be counted or measured in numerical values.

  • To Visualize: histogram, box plots, scatter plots
    • Discrete: countable
    • Continuous: measurable

• Qualitative data describes qualities or characteristics.

  • To Visualize: bar charts, dot charts
    • Nominal: labelled categories
    • Ordinal: ordered categories

1.2. Types of Statistics

graph LR
A[Types of statistics] --> B(Descriptive)
A --> C(Inferential)

• Descriptive Statistics: Summarizes the characteristics of a data set and gives information about raw data regarding its description or features.
  • Measures of central tendency
    • Mean
    • Median
    • Mode
  • Measures of dispersion
    • Range
    • Standard deviation
    • Variance
  • Frequency distributions
  • Histograms

• Inferential Statistics: the practice of using sampled data to draw conclusions or make predictions about a larger sample data sample or population.
  • Linear regression
  • Logistic regression
  • LDA & KNN
  • Decision trees and more..

1.3. Learning Types

graph
A[Machine Learning] --> B(Supervised Learning)
B --> E(Classification)
B --> F(Regression)
A --> C(Unsupervised Learning)
C --> G(Clustering)
C --> H(Dimensionality Reduction)
A --> D(Reinforcement Learning)

1-Supervised Learning: Use of labeled datasets to train algorithms that to classify data or predict outcomes accurately.

• Classification: It is the task of predicting a discrete class label. Examples, fraud detection, email spam detection, diagnostics, image classification.

  • K-Nearest Neighbor
  • Logistic Regression
  • Support Vector Machines
  • Decision Trees
  • Artificial Neural Networks

• Regression: It is the task of predicting a continuous quantity. Examples, risk assessment, score prediction.

  • Linear Regression
  • Polynomial Regression
  • Support Vector Regression
  • Decision Tree Regression
  • Multiple Linear Regression
  • Random Forest Regression

2-Unsupervised Learning: Use of machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.

• Clustering: It is identifying and grouping similar data points in larger datasets without concern for the specific outcome. Examples, biology, city planning, targetted marketing.

  • K-Means Clustering
  • Hierarchical Clustering

• Dimensionality Reduction: It is a statistical technique of reducing the amount of random variables in a problem by obtaining a set of principal variables. Examples, text mining, face recognition, image recognition, big data visualization.

  • PCA
  • Kernel PCA

3-Reinforcement Learning: It is a method based on rewarding desired behaviors and/or punishing undesired ones. A reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error. Examples, gaming, finance sector, inventory management, robot navigation.

• Q-Learning
• State-Action-Reward-State-Action (SARSA)
• Deep Q Network (DQN)

2. Steps in Building Machine Learning Model

1. Problem formulation
2. Data tidying
3. Data Preprocessing
   • Data Cleaning
     • Missing Data
     • Noisy Data
     • Outlier
   • Data Integration
   • Data Transformation
     • Aggregation
     • Normalization
     • Feature Selection
     • Discretization
     • Concept Hierarchy Generation
   • Data Reduction
     • Data Cube Aggregation
     • Attribute Subset Selection
     • Numerosity Reduction
     • Dimensionality Reduction
4. Train-Test Split
5. Model Building
6. Performance Metrics and Validation
7. Prediction

2.1. Data Preprocessing

Data preprocessing is a step in the data mining and data analysis process that takes raw data and transforms it into a format that can be understood and analyzed by computers and machine learning.

Data preprocessing is important to improve the overall data quality.

Why is data preprocessing important?

• Duplicate or missing values may give an incorrect view of the overall statistics of data.
• Outliers and inconsistent data points often tend to disturb the model’s overall learning, leading to false predictions.

Data preprocessing handles them.

There are four main steps of data preprocessing.

graph 
A(Data Preprocessing) --> B(Data Cleaning)
A --> C(Data Integration)
A --> D(Data Transformation)
A --> E(Data Reduction)

For more details on data preprocessing look here.