Predictive Modelling In Data Science By Spotle

This Spotle masterclass by industry and academic leaders is for people who want to build careers in data science

What you'll learn
Regression Analysis
Logistic Regression
Ridge Regression
Generalized Linear Model Or GLM Regression
Requirements
You will need to have a computer or a mobile handset with an internet connection
Description
In this Spotle masterclass you will learn:The predictive modeling, for a rewarding career in data science.Techniques such as linear regression, logistics regression, ridge regression, GLM regression.At the end of this course you will build predictive models in Python.Data science and Python have become key industry drivers in the global job and opportunity market. This course with mix of lectures from industry experts and Ivy League academics will help students, recent graduates and young professionals learn predictive modelling and its applications in business scenarios using Python programming language. What is predictive modelling? Predictive modeling is a process that uses statistics, data mining and probability to forecast the future outcomes. Such as, let’s say, an EdTech company is trying to assess the market. They have a question. The market condition that they predict will decide their future investment area. Their question is, is the future of learning going to be self-paced online? As we apply predictive modelling techniques on the available data from the market the predictive outcome to this question is ‘Yes. The future of learning is going to be self-paced online’. Now the future will have the actual outcome. But the EdTech company will invest in this area as per this predictive outcome.

Overview

Section 1: Introduction

Lecture 1 Introduction To Data Science

Lecture 2 Introduction To Predictive Modelling

Section 2: Linear Regression

Lecture 3 Introduction To Linear Regression

Lecture 4 Fit Linear Regression Model To Data

Lecture 5 Model Complexity And Bias-Variance Trade-off

Lecture 6 Variable Selection

Lecture 7 Statistical Inference

Lecture 8 Multicollinearity

Lecture 9 Measures of Accuracy

Lecture 10 Understanding Linear Regression With Python

Section 3: Logistic Regression

Lecture 11 Introduction To Logistic Regression

Lecture 12 Likelihood Estimation

Lecture 13 Statistical Inference

Lecture 14 Measure Of Accuracy

Lecture 15 Understanding Logistic Regression With Python

Section 4: Ridge Regression

Lecture 16 Ridge Regression Part - 1

Lecture 17 Ridge Regression Part - 2

Section 5: Generalized Linear Models

Lecture 18 Generalized Linear Models

Lecture 19 Understanding Generalized Linear Models With Python

Anyone with an interest in a rewarding career in Data Science