# Final Exam: Math Behind ML Algorithms

Math    |    Intermediate
• 1 video | 32s
• Includes Assessment
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.

## WHAT YOU WILL LEARN

• 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   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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