Brief Introduction
Learn the predictive modelling process in Python. Create the insights needed to compete in business.
Description
This course provides you with the skills to build a predictive model from the ground up, using Python.
You will learn the full lifecycle of building the model. First, you'll understand the data discovery process and discover how to make connections between the predicting and predicted variables. You will also learn about key data transformation and preparation issues, which form the backdrop to an introduction in Python for data analytics.
Through the analysis of real-life data, you will also develop an approach to implement simple linear and logistic regression models. These real-life examples include assessments on customer credit card behavior and case studies on sales volume forecasting.
This course is the first in the MicroMasters program and will prepare you for modeling classification and regression problems with statistical and machine learning methods.
Knowledge
- In this course you will:
- Understand the predictive analytics process
- Gather and prepare data for predictive modelling
- Clean datasets to prevent data quality issues in your models
- Implement linear and logistic refression models using real-life data
Microsoft Power BI - Business Intelligence for Beginners
- 4.4
-
![]()
![]()
![]()
![]()
![]()
Beginner Course on Power BI : Business Intelligence & Data/Business Analysis with Microsoft Power BI Do you want to build professional-quality Business Intelligence Reports from the ground up ?...
Fundamentals of Apache Flink
- 3.4
-
![]()
![]()
![]()
![]()
![]()
Use Apache Flink and its ecosystem to process real-time big data Have you used Flink, but want to learn how to set it up and use it properly?...
Mathematics & Statistics of Machine Learning & Data Science
- 4.2
-
![]()
![]()
![]()
![]()
![]()
Learn Mathematics and Statistics of Machine Learning, Artificial Intelligence, Neural Networks and Deep Learning || DATA SCIENCE || What Does a Data Scientist Do?...
Foundations of Sports Analytics: Data, Representation, and Models in Sports
- 4.5
-
![]()
![]()
![]()
![]()
![]()
This course provides an introduction to using Python to analyze team performance in sports. Introduction to Sports Performance and Data Introduction to Foundations and Instructor Stefan Szymanski...
