Machine Learning for BI, PART 3: Regression & Forecasting

Machine Learning for BI, PART 3: Regression & Forecasting

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2.5 Hours | 956 MB

Demystify Machine Learning and build foundational Data Science skills like regression & forecasting, without any code!

This course is PART 3 of a 4-PART SERIES designed to help you build a strong, foundational understanding of Machine Learning:

PART 1: QA & Data Profiling

PART 2: Classification

PART 3: Regression & Forecasting

PART 4: Unsupervised Learning (Coming Soon!)

This course makes data science approachable to everyday people, and is designed to demystify powerful Machine Learning tools & techniques without trying to teach you a coding language at the same time.

Instead, we’ll use familiar, user-friendly tools like Microsoft Excel to break down complex topics and help you understand exactly HOW and WHY machine learning works before you dive into programming languages like Python or R. Unlike most Data Science and Machine Learning courses, you won’t write a SINGLE LINE of code.

COURSE OUTLINE:

In this Part 3 course, we’ll start by introducing core building blocks like linear relationships and least squared error, then show you how these concepts can be applied to univariate, multivariate, and non-linear regression models.

From there we’ll review common diagnostic metrics like R-squared, mean error, F-significance, and P-Values, along with important concepts like homoscedasticity and multicollinearity.

Last but not least we’ll dive into time-series forecasting, and explore powerful techniques for identifying seasonality, predicting nonlinear trends, and measuring the impact of key business decisions using intervention analysis:

Section 1: Intro to Regression

  • Supervised Learning landscape
  • Regression vs. Classification
  • Feature engineering
  • Overfitting & Underfitting
  • Prediction vs. Root-Cause Analysis

Section 2: Regression Modeling 101

  • Linear Relationships
  • Least Squared Error (SSE)
  • Univariate Regression
  • Multivariate Regression
  • Nonlinear Transformation

Section 3: Model Diagnostics

  • R-Squared
  • Mean Error Metrics (MSE, MAE, MAPE)
  • Null Hypothesis
  • F-Significance
  • T-Values & P-Values
  • Homoskedasticity
  • Multicollinearity

Section 4: Time-Series Forecasting

  • Seasonality
  • Auto Correlation Function (ACF)
  • Linear Trending
  • Non-Linear Models (Gompertz)
  • Intervention Analysis

Throughout the course we’ll introduce hands-on case studies to solidify key concepts and tie them back to real world scenarios. You’ll see how regression analysis can be used to estimate property prices, forecast seasonal trends, predict sales for a new product launch, and even measure the business impact of a new website design.

If you’re ready to build the foundation for a successful career in Data Science, this is the course for you!

What you’ll learn

  • 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
  • Calculate diagnostic metrics like R-Squared, Mean Error, F-Significance and P-Values to diagnose model quality
  • Explore unique, hands-on case studies to see how regression analysis can be applied to real-world business intelligence use cases
Table of Contents

Getting Started
1 Course Structure & Outline
2 READ ME Important Notes for New Students
3 About This Series
4 DOWNLOAD Course Resources
5 Setting Expectations

Intro to Regression
6 Supervised vs. Unsupervised Learning
7 RECAP Key Concepts
8 Regression 101
9 Regression Workflow
10 Feature Engineering
11 Splitting & Overfitting
12 Prediction vs. Root-Cause Analysis

Regression Modeling
13 Intro to Regression Modeling
14 Linear Relationships
15 Least Squared Error
16 Univariate Linear Regression
17 CASE STUDY Univariate Linear Regression
18 Multiple Linear Regression
19 Non-Linear Regression
20 CASE STUDY Non-Linear Regression

Model Diagnostics
21 Intro to Model Diagnostics
22 Sample Model Output
23 R-Squared
24 Mean Error Metrics (MSE, MAE, MAPE)
25 Homoskedasticity
26 Null Hypothesis
27 F-Significance
28 T-Values & P-Values
29 Multicollinearity
30 Variance Inflation Factor
31 RECAP Sample Model Output

Time-Series Forecasting
32 Intro to Forecasting
33 Seasonality
34 Auto Correlation Function
35 CASE STUDY Seasonality with ACF
36 One-Hot Encoding
37 CASE STUDY Seasonality with One-Hot Encoding
38 Linear Trending
39 CASE STUDY Seasonality with Linear Trend
40 Smoothing
41 CASE STUDY Smoothing
42 Non-Linear Trends
43 CASE STUDY Non-Linear Trend
44 Intervention Analysis
45 CASE STUDY Intervention Analysis

Wrapping Up
46 Looking Ahead to Part 4
47 BONUS LECTURE

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