Machine Learning Python: Regression Modeling

1. Title: Title: Machine Learning Python: Regression Modeling About this Course Use Linear Regression to solve business problems and master the basics of Machine Learning The course "Machine Learning Basics: Building Regression Model in Python" teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems. In this course students will learn the following: How to predict future outcomes basis past data by implementing Simplest Machine Learning algorithm • How to do preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression • Understand how to interpret the result of Linear Regression model and translate them into actionable insight • Understanding of basics of statistics and concepts of Machine Learning • Learn advanced variations of OLS method of Linear Regression • Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python • This course is suitable for anyone curious about machine learning or professionals beginning their data journey. Basic knowledge Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same • What will you learn In this course students will learn the following: How to predict future outcomes basis past data by implementing Simplest Machine Learning algorithm • How to do preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression •

2. Understand how to interpret the result of Linear Regression model and translate them into actionable insight • Understanding of basics of statistics and concepts of Machine Learning • Learn advanced variations of OLS method of Linear Regression • Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python • Curriculum Welcome to the course! Course contents Types of Data Types of Statistics Describing data Graphically Measures of Centers Measures of Dispersion Installing Python and Anaconda Opening Jupyter Notebook Introduction to Jupyter Arithmetic operators in Python: Python Basics Strings in Python: Python Basics Lists, Tuples and Directories: Python Basics Working with Numpy Library of Python Working with Pandas Library of Python Working with Seaborn Library of Python Introduction to Machine Learning Building a Machine Learning Model Gathering Business Knowledge Data Exploration

3. The Dataset and the Data Dictionary Importing Data in Python Univariate analysis and EDD EDD in Python Outlier Treatment Outlier Treatment in Python Missing Value Imputation Missing Value Imputation in Python Bi-variate analysis and Variable transformation Variable transformation and deletion in Python Non-usable variables Dummy variable creation: Handling qualitative data Dummy variable creation in Python Correlation Analysis Correlation Analysis in Python The Problem Statement Basic Equations and Ordinary Least Squares (OLS) method Assessing accuracy of predicted coefficients Assessing Model Accuracy: RSE and R squared Simple Linear Regression in Python Multiple Linear Regression The F - statistic Interpreting results of Categorical variables Multiple Linear Regression in Python Test-train split Bias Variance trade-off

4. Test train split in Python Linear models other than OLS Subset selection techniques Shrinkage methods: Ridge and Lasso Ridge regression and Lasso in Python Total Duration: 07:13:43hours Number of Lectures: 51 Course Language: English Location: Online Course Includes: Certificate on Completion Category: IT & Software Systems Price: Actual Price $ 49.99 Discount Offer price $ 9.99 80% OFF learning-basics-regression-modeling-in-python Course URL:https://www.simpliv.com/machinelearning/machine- Contact Us: Simpliv Email: support@simpliv.com Phone: 76760-08458 Email: sudheer@simpliv.com Phone: 9036771917