# Final Exam: Math Behind ML Algorithms

Math    |    Intermediate
• 1 video | 32s
• Includes Assessment
Rating 5.0 of 1 users (1)
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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