Today we're going to introduce one of the most flexible statistical tools - the General Linear Model (or GLM). GLMs allow us to create many different models to help describe the world - you see them a lot in science, economics, and...

This video will introduce you to Python, machine learning, and Random Forest and discuss the live implementations, quizzes, and projects.
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This clip is from the chapter "Introduction to the Course" of the series "Machine...

This video explains decomposition of variability: SST, SSR, SSE.
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This clip is from the chapter "Linear Regression Analysis" of the series "R Programming for Statistics and Data Science".This section explains linear regression...

From the section: Introduction to Artificial Neural Networks (ANN). This section introduces Artificial Neural Networks. You will learn about Neural Network for Binary Classifications, Neural Network with PCA for Binary...

This video explains about decomposing the linear regression model - understanding its nuts and bolts.
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This clip is from the chapter "Subtleties of Regression Analysis" of the series "Statistics for Data Science and Business...

This video is about the final assumption—no multicollinearity.
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This clip is from the chapter "Assumptions for Linear Regression Analysis" of the series "Statistics for Data Science and Business Analysis".This section...

This video will introduce you to Python, machine learning, and Random Forest and discuss the live implementations, quizzes, and projects.
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This clip is from the chapter "Introduction to the Course" of the series "Machine...

In this video, we will cover machine learning model.
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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 this...

In this video, we will cover COVID19 regression with TensorFlow.

In this video, you will learn about the linear regression model.
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This clip is from the chapter "The Fundamentals of Regression Analysis" of the series "Statistics for Data Science and Business Analysis".This section...

This video explains working of the regression model.
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This clip is from the chapter "Machine Learning with Python" of the series "Python in Practice - 15 Projects to Master Python".This section focuses on machine learning with...

This video explains about the representational power and data utilization capacity of DNN.
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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...

In this video, we will cover representational power and data utilization capacity of DNN.
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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...

In this video, we will cover regression exercise.
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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 this...

In this video, we will cover derived features.
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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...

This video discusses how to determine the best statistical regression model to approximate data within a scatter plot and make predictions through interpolation and extrapolation. It covers the process of inputting data into a graphing...

In this video, the teacher explains the concept of residuals and how they can be used to assess the appropriateness of a linear regression model for a given data set. The teacher provides examples and demonstrates how to calculate...