Let's explore more about using two factors to perform the two-way ANOVA, which will let us find the influence of two factors.

In this lecture, we will look at a decision tree, how it works, its logic, and how to construct a decision tree.

Here, we will understand dependent and independent variables and learn how to predict values using linear regression.

Let's now learn more about the Random Forest, which is based on a decision tree (not very robust and more complex) that is now broken down into smaller decision trees, which are not as complex.

In this video, we will explore overfitting, which is very commonly used in machine learning.

After learning about simple linear regression with two variables, we will now look at multiple linear regression using multiple variables.

In this lecture, you will learn another important metric to determine the quality of our regression using the root mean square error method.

This is the solution to the practice exercise on the mean square error and mean absolute error to check the accuracy of our regression.

After learning about the measures of spread, the range, and IQR, we will now learn more about the two most common measures of spread, the variance, and standard deviation.

In this video, you will learn how to build a CloudWatch dashboard – lab.

A video entitled "Compare Populations" which shows ways to compare two populations of data.

Pupil outcome: I can construct a box plot. Key learning points: - A box plot requires 5 pieces of information - These are lowest value, lower quartile, median, upper quartile and highest value - Outliers should be included in the range...

Pupil outcome: I can use my knowledge of cumulative frequency and histograms to solve problems. Key learning points: - Different representations of the same data set give a fuller picture - It is important to identify what features are...

Learn how to perform linear and cubic interpolation on your data.

Learn how to perform Linear Regression in Python. Explore the performance metrics as well as use this technique to predict the result for new inputs.

Mixed costs are a combination of two or more related fixed and variable costs. Generally, you used the high-low method of a regression analysis to determine the relationship between fixed and variable costs.

This video explains how to use the least squared method to break down mixed costs in Managerial Accounting.

Explore how machine learning can recommend music based on similarity

I'm sure that we all agree that the best way to do data visualization is to use R programming, in particular, the ggplot2 package. To become an advanced ggplot user, you need to start controlling things like the titles, axis...

In this lesson, we will look at a target or label as output data and feature, a measurable property or characteristic of an element.
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This clip is from the chapter "Introduction to Machine Learning" of the series "Machine...

This video provides an introduction to the ML model lifecycle.
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This clip is from the chapter "Data Science Models with Google App Engine" of the series "Data Science Model Deployments and Cloud Computing on GCP".This section...

In this lesson, you will learn about a compilation of several views or charts with related information at one location. We will use views to create a simple dashboard.

Let's learn about a chart that is instantly recognizable to data scientists and statisticians: the distribution chart.

In this video, you will learn how to time the stock market and will focus on the coding aspect of how the algorithm is designed.
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This clip is from the chapter "Stock Market Timing" of the series "Introduction to FinTech Using...