# Regression Analysis Courses

## 1. 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

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

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

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

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

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

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

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

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

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 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 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

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

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

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

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.