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Machine Learning - Coggle Diagram

- - - - Machine Learning algorithm (Training)
        
        Trained Model
        
        Evaluation and Prediction
        
        If the prediction and results don’t match, the algorithm is re-trained multiple times until the data scientist gets the desired outcome
    - - Supervised Learning
        
        What
        
        requires the data scientist to train the algorithm with both labeled inputs and desired outputs
        
        Where
        
        Bioinformatics
        
        Speech Recognition
        
        Spam Detection
        
        sentiment analysis
        
        Object Detection
        
        weather forecasting and pricing prediction
        
        Why
        
        allows collecting data and produces data output from previous experiences.
        
        Helps to optimize performance criteria with the help of experience
        
        helps to solve various types of real-world computation problems.
        
        When
        
        Classification
        
        A classification problem is when the output variable is a category such as "Apple" or "Orange" or "disease" and "no disease"
        
        SVM
        
        Decision Trees
        
        Random Forest
        
        KNN
        
        Naïve Bayes
        
        Stochastic Gradient Descent
        
        Regression
        
        A regression problem is when the output variable is a real value, such as “dollars” or “weight”.
        
        Linear Regression
        
        Logistic Regresssion
        
        polynomial regression
    - - Unsupervised Learning
        
        What?
        
        requires the data scientist to train the algorithm with both labeled inputs and desired outputs
        
        Where
        
        anomaly detection
        
        recommendation engines
        
        Customer segmentation
        
        medical imaging,etc
        
        Why
        
        Classifying big data can be challenging.
        
        There is lesser complexity compared to the supervised learning task. Here, no one is required to interpret the associated labels and hence it holds lesser complexities.
        
        When
        
        Association
        
        An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.
        
        Apriori
        
        Clustering
        
        A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior
        
        K-means
        
        Hierarchical Clustering
        
        DBSCAN
        
        Dimensionality reduction
        
        is a learning technique used when the number of features (or dimensions) in a given dataset is too high. It reduces the number of data inputs to a manageable size while also preserving the data integrity
        
        Principal Component Analysis (PCA)
        
        Singular Value Decomposition (SVD), etc