Regression Analysis Courses

1. Deep Learning Prerequisites: Linear Regression in Python

Deep Learning Prerequisites: Linear Regression in Python

Data science, machine learning, and artificial intelligence in Python for students and professionals
Content:
- Derive and solve a linear regression model
- and apply it appropriately to data science problems
- Program your own version of a linear regression model in Python

2. The STATA OMNIBUS: Regression and Modelling with STATA

The STATA OMNIBUS: Regression and Modelling with STATA

4 COURSES IN 1! Includes introduction to Linear and Non-Linear Regression, Regression Modelling and STATA. Updated Freq.
Content:
- The theory behind linear and non-linear regression analysis.
- To be at ease with regression terminology.
- The assumptions and requirements of Ordinary Least Squares (OLS) regression.

3. Deep Learning Foundation : Linear Regression and Statistics

Deep Learning Foundation : Linear Regression and Statistics

Learn linear regression from scratch, Statistics, R-Squared, Python, Gradient descent, Deep Learning, Machine Learning
Content:
- Mathematics behind R-Squared
- Linear Regression
- VIF and more!
- Deep understating of Gradient descent and Optimization
- Program your own version of a linear regression model in Python

4. Complete Linear Regression Analysis in Python

Complete Linear Regression Analysis in Python

Linear Regression in Python| Simple Regression, Multiple Regression, Ridge Regression, Lasso and subset selection also
Content:
- Learn how to solve real life problem using the Linear Regression technique
- Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression
- Predict future outcomes basis past data by implementing Simplest Machine Learning algorithm

5. Regression Analysis / Data Analytics in Regression

Regression Analysis / Data Analytics in Regression

Gain Important and Highly Marketable Skills in Regression Analysis - Tame the Regression Beast Today!
Content:
- Understand when to use simple
- multiple
- and hierarchical regression
- Understand the meaning of R-Square and the role it plays in regression
- Assess a regression model for statistical significance
- including both the overall model and the individual predictors

6. Regression Analysis for Statistics & Machine Learning in R

Regression Analysis for Statistics & Machine Learning in R

Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in R
Content:
- Implement and infer Ordinary Least Square (OLS) regression using R
- Apply statistical and machine learning based regression models to deals with problems such as multicollinearity
- Carry out variable selection and assess model accuracy using techniques like cross-validation

7. Machine Learning Regression Masterclass in Python

Machine Learning Regression Masterclass in Python

Build 8+ Practical Projects and Master Machine Learning Regression Techniques Using Python, Scikit Learn and Keras
Content:
- Master Python programming and Scikit learn as applied to machine learning regression
- Understand the underlying theory behind simple and multiple linear regression techniques
- Apply simple linear regression techniques to predict product sales volume and vehicle fuel economy

8. ML for Business Managers: Build Regression model in R Studio

ML for Business Managers: Build Regression model in R Studio

Simple Regression & Multiple Regression| must-know for Machine Learning & Econometrics | Linear Regression in R studio
Content:
- Learn how to solve real life problem using the Linear Regression technique
- Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression
- Predict future outcomes basis past data by implementing Simplest Machine Learning algorithm

9. Linear Regression and Logistic Regression in Python

Linear Regression and Logistic Regression in Python

Build predictive ML models with no coding or maths background. Linear Regression and Logistic Regression for beginners
Content:
- Learn how to solve real life problem using the Linear and Logistic Regression technique
- Preliminary analysis of data using Univariate and Bivariate analysis before running regression analysis
- Understand how to interpret the result of Linear and Logistic Regression model and translate them into actionable insight

10. Linear Regression, GLMs and GAMs with R

Linear Regression, GLMs and GAMs with R

How to extend linear regression to specify and estimate generalized linear models and additive models.
Content:
- Understand the assumptions of ordinary least squares (OLS) linear regression.
- Specify
- estimate and interpret linear (regression) models using R.
- Understand how the assumptions of OLS regression are modified (relaxed) in order to specify
- estimate and interpret generalized linear models (GLMs).

11. Learn Statistics and Regression Modeling for Data Science

Learn Statistics and Regression Modeling for Data Science

Learn statistics and build regression models step by step through real business scenarios
Content:
- Learn about different types of Regression Models and their use
- Run Regression Analysis in several computer applications
- Learn in detail to build Linear Regression Model and Logistic Model which are highly used in business analysis

12. Linear Regression and Logistic Regression using R Studio

Linear Regression and Logistic Regression using R Studio

Linear Regression and Logistic Regression for beginners. Understand the difference between Regression & Classification
Content:
- Learn how to solve real life problem using the Linear and Logistic Regression technique
- Preliminary analysis of data using Univariate and Bivariate analysis before running regression analysis
- Graphically representing data in R before and after analysis

13. Machine Learning : Linear Regression using TensorFlow Python

Machine Learning : Linear Regression using TensorFlow Python

Design, Develop and Train the model
Content:
- Machine Learning - Linear Regression in TensorFlow with Python
- TensorFlow model for Linear Regression

14. Machine Learning for BI, PART 3: Regression & Forecasting

Machine Learning for BI, PART 3: Regression & Forecasting

Demystify Machine Learning and build foundational Data Science skills like regression & forecasting, without any code!
Content:
- Build foundational machine learning & data science skills
- without writing complex code
- Use intuitive
- user-friendly tools like Microsoft Excel to introduce & demystify machine learning tools & techniques
- Predict numerical outcomes using regression modeling and time-series forecasting techniques

15. Understanding Regression Techniques

Understanding Regression Techniques

An Introduction to Predictive Analytics for Data Scientists
Content:
- Understand what regression is
- Build linear regression models
- Build logistic regression models

16. Multiple Regression Analysis with Excel

Multiple Regression Analysis with Excel

Learn multiple regression analysis main concepts from basic to expert level through a practical course with Excel.
Content:
- Define stocks dependent or explained variable and calculate its mean
- standard deviation
- skewness and kurtosis descriptive statistics.
- Outline rates
- prices and macroeconomic independent or explanatory variables and calculate their descriptive statistics.
- Analyze multiple regression statistics output through coefficient of determination or R square
- adjusted R square and regression standard error metrics.