# 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.