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Genre: eLearning
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
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!
Who this course is for:
Anyone looking to learn the basics of machine learning through real-world demos and intuitive, crystal clear explanations
Data Analysts or BI experts looking to transition into data science or build a fundamental understanding of machine learning
R or Python users seeking a deeper understanding of the models and algorithms behind their code
Excel users who want to learn powerful tools for forecasting & predictive analytics
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nitro.download/view/1FD28644596A2AF/Machine-Learning-for-BI-PART-3-Regression-Forecasting.19.4.part3.rar
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