Final Exam: Math Behind ML Algorithms

Math    |    Intermediate
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Final Exam: Math Behind ML Algorithms will test your knowledge and application of the topics presented throughout the Math Behind ML Algorithms track of the Skillsoft Aspire Essential Math for Data Science Journey.


  • Discuss residuals in regression
    explore least square error
    compute the best fit with partial derivatives
    calculate r-squared of a regression model
    discuss the normal equation
    visualize correlations of features
    split train and test data and create computations
    perform regression and view the predicted values
    introduce gradient descent
    explore gradients
    standardise and shape data for gradient descent
    work through a calculation of an epoch
    implement a single epoch
    identify correlations for performing logistic regression
    calculate an s-curve in logistic regression
    set up training and testing data for logistic regression
    introduce the classification problem
    contrast rule-based and ml-based classifiers
    recall the structure of a decision tree
    define and understand entropy
    define and understand information gain
    define and calculate gini impurity
    recall characteristics of gini impurity
    split decision trees based on gini impurity
    decide splits for a rule-based decision tree
    define a rule-based decision tree
    introduce decision trees for continuous values
    train an ml-based decision tree
    recall how distance-based models work at a high level and identify the use cases of such models
    describe the hamming and cosine distance metrics
  • recount how the knn and k-means algorithms use distance metrics to perform ml operations
    define and visualize two points in a two-dimensional space using python
    calculate the euclidean and manhattan distance between two points using scipy as well as your own function
    analyze the data used to implement a classification model using k nearest neighbors
    implement a function that classifies a point using the k nearest neighbors algorithm
    classify test data points using your own knn classifier and evaluate the model using a variety of metrics
    code the individual steps involved in performing a clustering operation using the k-means algorithm
    define a function that clusters the points in a dataset using the k-means algorithm and then test it
    recognize the place of support vector machines (svms) in the machine learning landscape
    outline how svms can be used to classify data, how hyperplanes are defined, and the qualities of an optimum hyperplane
    recall the qualities of an optimum hyperplane, outline how scaling works with svm, distinguish soft and hard margins, and recognize when and how to use either margin
    recall the techniques that can be applied to classify data that are not linearly separable
    apply the gradient descent algorithm to solve for the optimum hyperplane
    use scikit-learn to generate blob data that is linearly separable
    separate a dataset into training and test sets
    load a dataset from a csv file into a pandas dataframe and analyze it in preparation for binary classification
    generate a heatmap to visualize the correlations between features in a dataset
    build and evaluate an svm classifier and recognize the importance of scaling the inputs to such a model
    use boxplots, a pair plot, and a heatmap to analyze a dataset in preparation for training a regression model
    recall the architecture and components that make up neural networks
    mathematical operation of a neuron
    compute the weighted sum of inputs with bias
    process data in batches and with multiple layers
    illustrate relu, leaky relu, and elu activation functions
    illustrate step, sigmoid, and tangent activation functions
    recall the characteristics of activation functions
    describe how unstable gradients can be mitigated using variants of the relu activation function
    create a simple neural network with one neuron for regression
    illustrate the impact of learning rate and number of epochs of training
    illustrate the classification dataset


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