Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
Our Courses
Mining Quality Prediction Using Machine & Deep Learning
In this 1.5-hour long project-based course, you will be able to: - Understand the theory and intuition behind Simple and Multiple Linear Regression. - Import Key python libraries, datasets and perform data visualization - Perform exploratory data analysis and standardize the training and testing data. - Train and Evaluate different regression models using Sci-kit Learn library. - Build and train an Artificial Neural Network to perform regression. - Understand the difference between various regression models KPIs such as MSE, RMSE, MAE, R2, and adjusted R2. - Assess the performance of regressio
-
Course by
-
Self Paced
-
2 hours
-
English
Necessary Condition Analysis (NCA)
Welcome to Necessary Condition Analysis (NCA). NCA analyzes data using necessity logic. A necessary condition implies that if the condition is not in place, there will be guaranteed failure of the outcome. The opposite however is not true; if the condition is in place, success of the outcome is not guaranteed. Examples of necessary conditions are a student’s GMAT score for admission to a PhD program; a student will not be admitted to a PhD program when his GMAT score is too low.
-
Course by
-
Self Paced
-
7 hours
-
English
Linear Regression and Multiple Linear Regression in Julia
This guided project is for those who want to learn how to use Julia for linear regression and multiple linear regression.
-
Course by
-
Self Paced
-
2 hours
-
English
XG-Boost 101: Used Cars Price Prediction
In this hands-on project, we will train 3 Machine Learning algorithms namely Multiple Linear Regression, Random Forest Regression, and XG-Boost to predict used cars prices.
-
Course by
-
Self Paced
-
3 hours
-
English
Data Analysis in R: Predictive Analysis with Regression
Increasingly, predictive analytics is shaping companies' decisions about limited resources. In this project, you will build a regression model to make predictions. We will start this hands-on project by exploring the dataset and creating visualizations for the dataset. By the end of this 2-hour-long project, you will be able to build and interpret the result of a simple linear regression model in R. Also, you will learn how to perform model assessments and check for assumptions using diagnostic plots.
-
Course by
-
Self Paced
-
3 hours
-
English
Building Statistical Models in R: Linear Regression
Welcome to this project-based course Building Statistical Models in R: Linear Regression. This is a hands-on project that introduces beginners to the world of statistical modeling. In this project, you will learn the basics of building statistical models in R. We will start this hands-on project by exploring the dataset and creating visualizations for the dataset. By the end of this 2-hour long project, you will understand how to build and interpret the result of simple linear regression models in R.
-
Course by
-
Self Paced
-
3 hours
-
English
Introduction to Predictive Modeling
Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization. This course will introduce to you the concepts, processes, and applications of predictive modeling, with a focus on linear regression and time series forecasting models and their practical use in Microsoft Excel.
-
Course by
-
Self Paced
-
12 hours
-
English
Python and Statistics for Financial Analysis
Course Overview: https://youtu.be/JgFV5qzAYno Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry.
-
Course by
-
Self Paced
-
13 hours
-
English
Multiple Linear Regression with scikit-learn
In this 2-hour long project-based course, you will build and evaluate multiple linear regression models using Python.
-
Course by
-
Self Paced
-
3 hours
-
English
University Admission Prediction Using Multiple Linear Regression
In this hands-on guided project, we will train regression models to find the probability of a student getting accepted into a particular university based on their profile.
-
Course by
-
Self Paced
-
3 hours
-
English
The Classical Linear Regression Model
In this course, you will discover the type of questions that econometrics can answer, and the different types of data you might use: time series, cross-sectional, and longitudinal data.
-
Course by
-
Self Paced
-
22 hours
-
English
Linear Regression and Modeling
This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother?
-
Course by
-
Self Paced
-
10 hours
-
English