Regression Analysis with Python
(REGPYTHON.AJ1)
/ ISBN: 9781616916886
Regression Analysis with Python
Get the knowledge to use Python for building fast and better linear models and to deploy the resulting models in Python with uCertify’s course Regression Analysis with Python. The course provides handson experience of the concepts, Regression – The Workhorse of Data Science, Approaching Simple Linear Regression, Multiple Regression in Action. Logistic Regression, Data Preparation, Achieving Generalization, and so on.
Lessons

10+ Lessons

52+ Exercises

60+ Quizzes

38+ Flashcards

38+ Glossary of terms
TestPrep

35+ Pre Assessment Questions

35+ Post Assessment Questions
Lab

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 probabilitybased 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 outofsample data
 Greedy selection of features
 Regularization optimized by gridsearch
 Stability selection
 Summary
 Batch learning
 Online minibatch 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 OneColumn 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 Scikitlearn
 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 Scikitlearn Preprocessing Module
 Printing Standardized Coefficients
 Obtaining the Rsquared Baseline
 Recording Coefficient of Determination Using Rsquared
 Reporting All Rsquared 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 FourClass 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 ThirdDegree Polynomial Expansion
 Plotting the Distribution of Scores
 Demonstrating Working of Recursive Elimination
 Implementing L2 Regularization
 Performing Random Grid Search
 Demonstrating MiniBatch 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
×