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Core Concept of Machine Learning

Three components of machine learning

  • Data
  • Features
  • Algorithms

Three Components

Learning vs Intelligence

  • Aritificial intelligence is the name of a whole knowledge field, similar to biology or chemistry

  • Machine Learning is a part of artificial intelligence. An important part, but not the only one.

  • Neural Networks are one of machine learning types. A popular one, but there are other good guys in the class.

  • Deep Learning is a modern method of building, training, and using neural networks.

relationships

The map of the machine learning world

  • Full view of machine learning

machine learning map

  • Four main directions in machine learning

Four main directions

Part 1. Classic Machine Learning

classical machine learning

1.1 Supervised Learning

the machine has a “supervisor” or a “teacher” who gives the machine all the answers, like whether it’s a cat in the picture or a dog. The teacher has already divided (labeled) the data into cats and dogs, and the machine is using these examples to learn. One by one. Dog by cat.

  • There are two types of such tasks: classification – an object’s category prediction, and regression – prediction of a specific point on a numeric axis.

Classifications

  • “Splits objects based at one of the attributes known beforehand. Separate socks by based on color, documents based on language, music by genre”

classification

  • Today used for:

    • Spam filtering
    • Language detection
    • A search of similar documents
    • Sentiment analysis
    • Recognition of handwritten characters and numbers
    • Fraud detection

  • Popular algorithms:

    • Naive Bayes
    • Decision Tree
    • Logistic Regression
    • K-Nearest Neighbours
    • Support Vector Machine

  • Support Vector Machines (SVM) is rightfully the most popular method of classical classification. It was used to classify everything in existence: plants by appearance in photos, documents by categories, etc.

Regression

  • “Draw a line through these dots. Yep, that’s the machine learning”

regression

  • Today this is used for:

    • Stock price forecasts
    • Demand and sales volume analysis
    • Medical diagnosis
    • Any number-time correlations
    • Popular algorithms are linear and Polynomial regressions.

1.2 Unsupervised Learning

Clustering

  • “Divides objects based on unknown features. Machine chooses the best way”

clustering

  • Nowadays used:

    • For market segmentation (types of customers, loyalty)
    • To merge close points on a map
    • For image compression
    • To analyze and label new data
    • To detect abnormal behavior

popular algorithms: K-means_clustering, Mean-Shift, DBSCAN * The 5 Clustering Algorithms Data Scientists Need to Know

Dimensionality-Reduction(Generalization)

  • “Assembles specific features into more high-level ones”

  • Nowadays is used for:

    • Recommender systems (★)
    • Beautiful visualizations
    • Topic modeling and similar document search
    • Fake image analysis
    • Risk management

  • Popular algorithms: Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Latent Dirichlet allocation (LDA), Latent Semantic Analysis (LSA, pLSA, GLSA), t-SNE (for visualization)