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Regression Analysis with Python
(REG-PYTHON.AJ1)
/ ISBN: 9781616916886
This course includes
Lessons
TestPrep
Lab
Regression Analysis with Python
Lessons
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10+ Lessons
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52+ Exercises
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60+ Quizzes
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38+ Flashcards
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38+ Glossary of terms
TestPrep
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35+ Pre Assessment Questions
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35+ Post Assessment Questions
Lab
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61+ Performance Lab Python
- What this course covers
- What you need for this course
- Who this course is for
- Conventions
- Regression analysis and data science
- Python for data science
- Python packages and functions for linear models
- Summary
- Defining a regression problem
- Starting from the basics
- Extending to linear regression
- Minimizing the cost function
- Summary
- Using multiple features
- Revisiting gradient descent
- Estimating feature importance
- Interaction models
- Polynomial regression
- Summary
- Defining a classification problem
- Defining a probability-based approach
- Revisiting gradient descent
- Multiclass Logistic Regression
- An example
- Summary
- Numeric feature scaling
- Qualitative feature encoding
- Numeric feature transformation
- Missing data
- Outliers
- Summary
- Checking on out-of-sample data
- Greedy selection of features
- Regularization optimized by grid-search
- Stability selection
- Summary
- Batch learning
- Online mini-batch learning
- Summary
- Least Angle Regression
- Bayesian regression
- SGD classification with hinge loss
- Regression trees (CART)
- Bagging and boosting
- Gradient Boosting Regressor with LAD
- Summary
- Downloading the datasets
- A regression problem
- An imbalanced and multiclass classification problem
- A ranking problem
- A time series problem
- Summary
Performance Lab Python
- Creating a One-Column Matrix Structure
- Visualizing the Distribution of Errors
- Plotting a Normal Distribution Graph
- Plotting a Scatterplot
- Standardizing a Variable
- Showing Regression Analysis Parameters
- Showing the Summary of Regression Analysis
- Printing the Residual Sum of Squared Errors
- Plotting Standardized Residuals
- Predicting with a Regression Model
- Regressing with Scikit-learn
- Using the fmin Minimization Procedure
- Finding Mean and Median
- Obtaining the Inverse of a Matrix
- Printing Eigenvalues
- Visualizing the Correlation Matrix
- Obtaining the Correlation Matrix
- Standardizing Using the Scikit-learn Preprocessing Module
- Printing Standardized Coefficients
- Obtaining the R-squared Baseline
- Recording Coefficient of Determination Using R-squared
- Reporting All R-squared Increment Above 0.03
- Representing LSTAT Using the Scatterplot
- Testing Degree of a Polynomial
- Creating a Dummy Dataset
- Obtaining a Classification Report
- Representing a Confusion Matrix Using Heatmap
- Creating a Confusion Matrix
- Plotting the sigmoid Function
- Fitting a Multiple Linear Regressor
- Creating and Fitting a Logistic Regressor Classifier
- Obtaining the Feature Vector and its Original and Predicted Labels
- Visualizing Multiclass Logistic Regressor
- Creating a Dummy Four-Class Dataset
- Centering the Variables
- Demonstrating the Logistic Regression
- Analyzing Qualitative Data Using Logit
- Transforming Qualitative Data
- Using LabelBinarizer
- Using the Hashing Trick
- Obtaining Residuals
- Replacing Missing Values With the Mean Value
- Representing Outliers Among Predictors
- Showing Outliers
- Splitting a Dataset
- Bootstrapping a Dataset
- Applying Third-Degree Polynomial Expansion
- Plotting the Distribution of Scores
- Demonstrating Working of Recursive Elimination
- Implementing L2 Regularization
- Performing Random Grid Search
- Demonstrating Mini-Batch Learning
- Obtaining LARS Coefficients
- Using Bayesian Regression
- Using the SGDClassifier Class With the hinge Loss
- Implementing SVR
- Implementing CART
- Implementing Random Forest Regressor
- Implementing Bagging
- Implementing Boosting
- Implementing Gradient Boosting Regressor with LAD
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