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Analytic Approach: It represents a problem in the context of statistical techniques and machine
learning so that the organisation can determine the most appropriate for the desired outcome.
Capstone Project: A capstone pro ect is a comprehensive, independent, and final pro ect
undertaken as part of the curriculum designed to assess the skills, knowledge, and expertise a
student has acquired.
Cross-validation: It is a resampling technique for evaluating machine learning models on a small
sample of data.
Data Collection: It is a process in which the data scientists identify available data sources
(structured, unstructured, and semi-structured) relevant to the problem area.
Data Storytelling: It is a means of delivering facts ith a compelling narrative to a specific
audience.
Data Understanding: It is a process in which the techniques such as descriptive statistics
and visualisations can be applied to datasets to evaluate the content, quality, and initial insights
of the data.
Deployment: It is the process in which a model is deployed into the production environment or
an equivalent test environment once it has been built and authorised by the business sponsors.
Design Thinking: It is a methodology that provides a solution-based approach for solving
problems.
Hyperparameters: These are parameters whose values govern the learning process.
Loss Function: It is a way of determining how well a certain algorithm models the data.
Mean Squared Error: It is the most basic and widely-used loss function, and it is frequently
taught in Machine Learning courses.
Modelling: It is the process through which several models based on graphical data can be
constructed and even tested for advantages and disadvantages.
Root Mean Square Error: It is a metric for determining ho ell a regression line fits the data
points.
Training Dataset: It is used to fine tune the machine learning model and train the algorithm.
Train Test Split Evaluation: It is a procedure that measures the performance of machine learning
algorithms when they need to make predictions on data that were not used to train the model.
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