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

This is the solution to the practice exercise to estimate how well a regression works using the root mean square error method.

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

After learning about linear regression and how to calculate the formula for our regression lines, we will now look at the correlation coefficient or Pearson's coefficient.

In this video, we will briefly understand the concept of regression and how regression analysis predicts data value.

While performing the hypothesis testing and general statistics, you will learn more about the p-z combination and the normal distribution.

After exploring confidence intervals and margin of error, we will now understand how to determine a sample size to reduce the error size caused by the sample size.

This brief introduction to distribution elaborates on what constitutes data distribution and the kinds of distribution you will learn about.

Here, we will understand the concepts of statistical moments, which generalizes what we already know about the central tendency measures and spread/dispersion.

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.

This lecture will explain the fundamental difference between a sample and a population in statistical data.

In this lesson, we will understand the measures of spread or dispersion, which is the range, and we will look at the interquartile range in detail.

In this lesson, the author explains more about the mean and median and when to use them for the best results.

In this lesson, we will discuss the second important measure of central tendency, the median.

In this video, we will discuss the measures of central tendency. Here, you will learn more about the first measure, the mean.

This brief introduction to the section outlines what you will be learning from this section, including the basics of statistics.

In this video, the author briefly introduces what it would take to become a data scientist and then goes over in detail what one can learn from this course.

This is a welcome introduction by the author where he explains in detail what the course is all about, what you will learn from the course, and a brief outline of the entire course's content.

In this lesson, we will begin inputting the data we will use for our rule-based chatbot.

Let's explore the Natural Language Toolkit, a library used for specific tasks related to natural language processing, including tokenization, filtering words, stemming, lemmatization, chunking/chinking, and entity recognition.

After collecting all the data, you will learn to eliminate the text that we do not want to use in the chatbot project through an elimination process.

This video explains digital signatures.

In this video, we will be exploring how to use POST requests to send and receive data from an API using Express.js.

In this video, we will be exploring the basics of JSON, or JavaScript Object Notation, and how it's used in web development.