Python Machine Learning Solutions

The Realm of Supervised Learning 10 lectures 36:15 The Course Overview This video gives an overview of the entire course Preprocessing Data Using Different Techniques Machine learning algorithms need processed data for operation. Let’s explore how to process raw data in this video. • Look at Mean Removal • Go through Scaling • Learn Data Normalization and Binarization Label Encoding Algorithms need data in numerical form to use them directly. But we often label data with words. So, let’s see how we transform word labels into numerical form. • Import a preprocessing package in a new file • Create labels • Encode the labels Building a Linear Regressor Linear regression uses a linear combination of input variables to estimate the underlying function that governs the mapping from input to output. Our aim would be to identify that relationship between input data and output data. • Load the data and label into variables • Separate the training dataset and the testing dataset • Check the test dataset output Regression Accuracy and Model Persistence There are some cases where there is difference between actual values and values predicted by regressor. We need to keep a check on its accuracy. This video will enable us to do that. • Identify metrics to evaluate regressor • Compute the metrics • Achieving Model Persistence programmatically Building a Ridge Regressor Linear regressors tend to be inaccurate sometimes, as outliers disrupt the model. We need to regularize this. We will see that in this video. • Create a ridge regressor • Initialize the alpha parameter • Train the regressor Building a Polynomial Regressor Linear model fails to capture the natural curve of datapoints, which makes it quite inaccurate. So, let’s go through polynomial regressor to see how we can improve that. • Observe the polynomial regression model • Initialize a polynomial of a certain degree • Measure the accuracy of the model Estimating housing prices Applying regression concepts to solve real-world problems can be quite tricky. We will explore how to do it successfully. • Get a standard housing dataset and divide it into input and output • Fit a decision tree regression model • Evaluate the performance of AdaBoost Computing relative importance of features We don’t really have an idea on which feature contributes to the output and which doesn’t. It becomes critical to know that, in case we’ve to omit one. This video will help you compute their relative importance. • Plotting the relative importance of features • Scale values from the feature_importances_ method • Compare the output of the decision tree regressor with that of AdaBoost Estimating bicycle demand distribution There might be some problems where the basic regression methods we’ve learned won’t help. One such problem is bicycle demand distribution. You will see how to solve that here. • Import csv, RandomForestRegressor and plot_feature_importances • Train the regressor and evaluate its performance • Plot the importances feature with varying dataset. The Realm of Supervised Learning. 10 lectures 36:15 The Course Overview This video gives an overview of the entire course Preprocessing Data Using Different Techniques Machine learning algorithms need processed data for operation. Let’s explore how to process raw data in this video. • Look at Mean Removal • Go through Scaling • Learn Data Normalization and Binarization Label Encoding Algorithms need data in numerical form to use them directly. But we often label data with words. So, let’s see how we transform word labels into numerical form. • Import a preprocessing package in a new file • Create labels • Encode the labels Building a Linear Regressor Linear regression uses a linear combination of input variables to estimate the underlying function that governs the mapping from input to output. Our aim would be to identify that relationship between input data and output data. • Load the data and label into variables • Separate the training dataset and the testing dataset • Check the test dataset output Regression Accuracy and Model Persistence There are some cases where there is difference between actual values and values predicted by regressor. We need to keep a check on its accuracy. This video will enable us to do that. • Identify metrics to evaluate regressor • Compute the metrics • Achieving Model Persistence programmatically Building a Ridge Regressor Linear regressors tend to be inaccurate sometimes, as outliers disrupt the model. We need to regularize this. We will see that in this video. • Create a ridge regressor • Initialize the alpha parameter • Train the regressor Building a Polynomial Regressor Linear model fails to capture the natural curve of datapoints, which makes it quite inaccurate. So, let’s go through polynomial regressor to see how we can improve that. • Observe the polynomial regression model • Initialize a polynomial of a certain degree • Measure the accuracy of the model Estimating housing prices Applying regression concepts to solve real-world problems can be quite tricky. We will explore how to do it successfully. • Get a standard housing dataset and divide it into input and output • Fit a decision tree regression model • Evaluate the performance of AdaBoost Computing relative importance of features We don’t really have an idea on which feature contributes to the output and which doesn’t. It becomes critical to know that, in case we’ve to omit one. This video will help you compute their relative importance. • Plotting the relative importance of features • Scale values from the feature_importances_ method • Compare the output of the decision tree regressor with that of AdaBoost Estimating bicycle demand distribution There might be some problems where the basic regression methods we’ve learned won’t help. One such problem is bicycle demand distribution. You will see how to solve that here. • Import csv, RandomForestRegressor and plot_feature_importances • Train the regressor and evaluate its performance • Plot the importances feature with varying dataset. The Course Overview This video gives an overview of the entire course The Course Overview This video gives an overview of the entire course The Course Overview This video gives an overview of the entire course The Course Overview This video gives an overview of the entire course This video gives an overview of the entire course This video gives an overview of the entire course Preprocessing Data Using Different Techniques Machine learning algorithms need processed data for operation. Let’s explore how to process raw data in this video. • Look at Mean Removal • Go through Scaling • Learn Data Normalization and Binarization Preprocessing Data Using Different Techniques Machine learning algorithms need processed data for operation. Let’s explore how to process raw data in this video. • Look at Mean Removal • Go through Scaling • Learn Data Normalization and Binarization Preprocessing Data Using Different Techniques Machine learning algorithms need processed data for operation. Let’s explore how to process raw data in this video. • Look at Mean Removal • Go through Scaling • Learn Data Normalization and Binarization Preprocessing Data Using Different Techniques Machine learning algorithms need processed data for operation. Let’s explore how to process raw data in this video. • Look at Mean Removal • Go through Scaling • Learn Data Normalization and Binarization Machine learning algorithms need processed data for operation. Let’s explore how to process raw data in this video. • Look at Mean Removal • Go through Scaling • Learn Data Normalization and Binarization Machine learning algorithms need processed data for operation. Let’s explore how to process raw data in this video. • Look at Mean Removal • Go through Scaling • Learn Data Normalization and Binarization Label Encoding Algorithms need data in numerical form to use them directly. But we often label data with words. So, let’s see how we transform word labels into numerical form. • Import a preprocessing package in a new file • Create labels • Encode the labels Label Encoding Algorithms need data in numerical form to use them directly. But we often label data with words. So, let’s see how we transform word labels into numerical form. • Import a preprocessing package in a new file • Create labels • Encode the labels Label Encoding Algorithms need data in numerical form to use them directly. But we often label data with words. So, let’s see how we transform word labels into numerical form. • Import a preprocessing package in a new file • Create labels • Encode the labels Label Encoding Algorithms need data in numerical form to use them directly. But we often label data with words. So, let’s see how we transform word labels into numerical form. • Import a preprocessing package in a new file • Create labels • Encode the labels Algorithms need data in numerical form to use them directly. But we often label data with words. So, let’s see how we transform word labels into numerical form. • Import a preprocessing package in a new file • Create labels • Encode the labels Algorithms need data in numerical form to use them directly. But we often label data with words. So, let’s see how we transform word labels into numerical form. • Import a preprocessing package in a new file • Create labels • Encode the labels Building a Linear Regressor Linear regression uses a linear combination of input variables to estimate the underlying function that governs the mapping from input to output. Our aim would be to identify that relationship between input data and output data. • Load the data and label into variables • Separate the training dataset and the testing dataset • Check the test dataset output Building a Linear Regressor Linear regression uses a linear combination of input variables to estimate the underlying function that governs the mapping from input to output. Our aim would be to identify that relationship between input data and output data. • Load the data and label into variables • Separate the training dataset and the testing dataset • Check the test dataset output Building a Linear Regressor Linear regression uses a linear combination of input variables to estimate the underlying function that governs the mapping from input to output. Our aim would be to identify that relationship between input data and output data. • Load the data and label into variables • Separate the training dataset and the testing dataset • Check the test dataset output Building a Linear Regressor Linear regression uses a linear combination of input variables to estimate the underlying function that governs the mapping from input to output. Our aim would be to identify that relationship between input data and output data. • Load the data and label into variables • Separate the training dataset and the testing dataset • Check the test dataset output Linear regression uses a linear combination of input variables to estimate the underlying function that governs the mapping from input to output. Our aim would be to identify that relationship between input data and output data. • Load the data and label into variables • Separate the training dataset and the testing dataset • Check the test dataset output Linear regression uses a linear combination of input variables to estimate the underlying function that governs the mapping from input to output. Our aim would be to identify that relationship between input data and output data. • Load the data and label into variables • Separate the training dataset and the testing dataset • Check the test dataset output Regression Accuracy and Model Persistence There are some cases where there is difference between actual values and values predicted by regressor. We need to keep a check on its accuracy. This video will enable us to do that. • Identify metrics to evaluate regressor • Compute the metrics • Achieving Model Persistence programmatically Regression Accuracy and Model Persistence There are some cases where there is difference between actual values and values predicted by regressor. We need to keep a check on its accuracy. This video will enable us to do that. • Identify metrics to evaluate regressor • Compute the metrics • Achieving Model Persistence programmatically Regression Accuracy and Model Persistence There are some cases where there is difference between actual values and values predicted by regressor. We need to keep a check on its accuracy. This video will enable us to do that. • Identify metrics to evaluate regressor • Compute the metrics • Achieving Model Persistence programmatically Regression Accuracy and Model Persistence There are some cases where there is difference between actual values and values predicted by regressor. We need to keep a check on its accuracy. This video will enable us to do that. • Identify metrics to evaluate regressor • Compute the metrics • Achieving Model Persistence programmatically There are some cases where there is difference between actual values and values predicted by regressor. We need to keep a check on its accuracy. This video will enable us to do that. • Identify metrics to evaluate regressor • Compute the metrics • Achieving Model Persistence programmatically There are some cases where there is difference between actual values and values predicted by regressor. We need to keep a check on its accuracy. This video will enable us to do that. • Identify metrics to evaluate regressor • Compute the metrics • Achieving Model Persistence programmatically Building a Ridge Regressor Linear regressors tend to be inaccurate sometimes, as outliers disrupt the model. We need to regularize this. We will see that in this video ? Get a standard housing dataset and divide it into input and output • Fit a decision tree regression model • Evaluate the performance of...