In this video, we will cover Matplotlib 3D surface plots.
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This clip is from the chapter "Basics for Data Science: Data Understanding and Data Visualization with Python" of the series "Data Science and Machine Learning (Theory...

In this video, we will cover regression practice with Python.
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This clip is from the chapter "Machine Learning: Machine Learning Crash Course" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...

This video explains applying filters – IndexFilter, BooleanFilter, GroupFilter.
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This clip is from the chapter "Working with the Beautiful and Powerful Bokeh Library" of the series "Data Analytics Using Python...

This video explains the Support Vector Machine project.
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This clip is from the chapter "Advanced Classification Techniques – Support Vector Machine" of the series "Practical Data Science Using Python".This section explains...

This video explains K-Means clustering optimization.
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This clip is from the chapter "Unsupervised Learning - K-Means Clustering" of the series "Practical Data Science Using Python".This section explains unsupervised learning -...

This video explains the implementation of Perceptron.
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This clip is from the chapter "DNN Foundation for Deep RL" of the series "Reinforcement Learning and Deep RL Python (Theory and Projects)".This section focuses on the DNN...

This video explains how to add data transfer objects.<br<br/>/>

This clip is from the chapter "Create API Endpoints" of the series "Minimal API Development with ASP.NET Core".This section focuses on creating API endpoints.

In this video, we will cover supervised PCA and Fishers linear discriminant analysis.
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science...

In this video, we will cover PCA properties.
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and...

In this video, we will cover PCA for small sample size problems (DualPCA).
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine...

In this video, we will cover Perceptron implementation.
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This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...

In this video, we will cover an overfitting example in Python.
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This clip is from the chapter "Machine Learning: Machine Learning Crash Course" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...

In this video, we will cover Eigen Space.
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A...

In this video, we will cover clustering practice with Python.
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This clip is from the chapter "Machine Learning: Machine Learning Crash Course" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...

In this lesson, students will learn how to use data and the line of best fit to predict the impact of additional study hours on test scores. They will understand that an exact relationship between variables guarantees a specific outcome,...

In this video, the teacher explains how to determine if a distribution is normally distributed by applying the empirical rule. The video covers the conditions that must be checked. Examples are provided to illustrate the application of...

In this video, we will discuss some widely used statistical concepts required in predictive analytics, followed by some basic understanding of predictive modeling, such as random sampling, central limit theorem, hypothesis testing...

In this video, the teacher explains how to calculate the line of best fit for a scatter plot using the method of least squares. They discuss different types of functions that can fit scatter plots, such as linear, quadratic, and...

In this video, students learn how to represent outliers in a boxplot by using quartiles. The video explains the five-number summary of a boxplot and demonstrates how to calculate the interquartile range. It then shows how to identify and...

This video demonstrates how to compare center and spread of histograms using the example of high school students' expected age of marriage compared to the age at which people 65 and over actually got married. The video explains how to...

In this video, the teacher explains how to find lines of fit and use them to make predictions based on data. The lesson includes examples and tips for drawing the best fit line and finding the slope and Y-intercept of the line. Students...

This video explains how to calculate the mean or average of a data set. The instructor explains the formula for calculating the mean using examples such as finding the mean of a set of numbers or the race times of female triathletes. The...

Explains how to read a boxplot including all the parameters associated with it.

Learn how to write the equation of a trend line from data in a scatterplot using point-slope form. Understand the concepts of linear equations, scatterplots, positive and negative correlations, and point-slope form. Through examples and...