Final Exam: Math Behind ML Algorithms
Math | Intermediate
- 1 Video | 32s
- Includes Assessment
- Earns a Badge
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 regressionexplore least square errorcompute the best fit with partial derivativescalculate R-squared of a regression modeldiscuss the normal equationvisualize correlations of featuressplit train and test data and create computationsperform regression and view the predicted valuesintroduce gradient descentexplore gradientsstandardise and shape data for gradient descentwork through a calculation of an epochimplement a single epochidentify correlations for performing logistic regressioncalculate an S-curve in logistic regressionset up training and testing data for logistic regressionintroduce the classification problemcontrast rule-based and ML-based classifiersrecall the structure of a decision treedefine and understand entropydefine and understand information gaindefine and calculate GINI impurityrecall characteristics of GINI impuritysplit decision trees based on GINI impuritydecide splits for a rule-based decision treedefine a rule-based decision treeintroduce decision trees for continuous valuestrain an ML-based decision treerecall how distance-based models work at a high level and identify the use cases of such modelsdescribe the Hamming and Cosine distance metrics
recount how the KNN and K-means algorithms use distance metrics to perform ML operationsdefine and visualize two points in a two-dimensional space using Pythoncalculate the Euclidean and Manhattan distance between two points using SciPy as well as your own functionanalyze the data used to implement a classification model using K Nearest Neighborsimplement a function that classifies a point using the K Nearest Neighbors algorithmclassify test data points using your own KNN classifier and evaluate the model using a variety of metricscode the individual steps involved in performing a clustering operation using the K-means algorithmdefine a function that clusters the points in a dataset using the K-means algorithm and then test itrecognize the place of support vector machines (SVMs) in the machine learning landscapeoutline how SVMs can be used to classify data, how hyperplanes are defined, and the qualities of an optimum hyperplanerecall 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 marginrecall the techniques that can be applied to classify data that are not linearly separableapply the gradient descent algorithm to solve for the optimum hyperplaneuse scikit-learn to generate blob data that is linearly separableseparate a dataset into training and test setsload a dataset from a CSV file into a pandas DataFrame and analyze it in preparation for binary classificationgenerate a heatmap to visualize the correlations between features in a datasetbuild and evaluate an SVM classifier and recognize the importance of scaling the inputs to such a modeluse boxplots, a pair plot, and a heatmap to analyze a dataset in preparation for training a regression modelrecall the architecture and components that make up neural networksMathematical operation of a neuroncompute the weighted sum of inputs with biasprocess data in batches and with multiple layersillustrate ReLU, Leaky ReLU, and ELU activation functionsillustrate step, sigmoid, and tangent activation functionsrecall the characteristics of activation functionsdescribe how unstable gradients can be mitigated using variants of the ReLU activation functioncreate a simple neural network with one neuron for regressionillustrate the impact of learning rate and number of epochs of trainingillustrate the classification dataset
IN THIS COURSE
1.Math Behind ML Algorithms33sUP NEXT
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platformDigital badges are yours to keep, forever.