Machine Learning for BI, PART 2: Classification Modeling

Machine Learning for BI, PART 2: Classification Modeling

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2.5 Hours | 778 MB

Demystify Machine Learning and build foundational Data Science skills for classification & prediction, without any code!

If you’re excited to explore Data Science & Machine Learning but anxious about learning complex programming languages or intimidated by terms like “naive bayes”, “logistic regression”, “KNN” and “decision trees”, you’re in the right place.

This course is PART 2 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.


In this Part 2 course, we’ll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting.

From there we’ll review common classification models including K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression and Sentiment Analysis, and share tips for model scoring, selection, and optimization.

Section 1: Intro to Classification

  • Supervised Learning landscape
  • Classification workflow
  • Feature engineering
  • Data splitting
  • Overfitting & Underfitting

Section 2: Classification Models

  • K-Nearest Neighbors
  • Naïve Bayes
  • Decision Trees
  • Random Forests
  • Logistic Regression
  • Sentiment Analysis

Section 3: Model Selection & Tuning

  • Hyperparameter tuning
  • Imbalanced classes
  • Confusion matrices
  • Accuracy, Precision & recall
  • Model selection & drift

Throughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll help build a recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for a travel company, extract sentiment from customer reviews, and much more.

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
  • Enrich datasets by using feature engineering techniques like one-hot encoding, scaling, and discretization
  • Predict categorical outcomes using classification models like K-nearest neighbors, naïve bayes, decision trees, and more
  • Apply techniques for selecting & tuning classification models to optimize performance, reduce bias, and minimize drift
  • Calculate metrics like accuracy, precision and recall to measure model performance
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 Classification
6 Supervised vs. Unsupervised Learning
7 Classification vs. Regression
8 RECAP Key Concepts
9 Classification 101
10 Classification Workflow
11 Feature Engineering
12 Data Splitting
13 Overfitting

Classification Models
14 Common Classification Models
15 Intro to K-Nearest Neighbors (KNN)
16 KNN Examples
18 Intro to Naïve Bayes
19 Naïve Bayes Frequency Tables
20 Naïve Bayes Conditional Probability
21 CASE STUDY Naïve Bayes
22 Intro to Decision Trees
23 Decision Trees Entropy 101
24 Entropy & Information Gain
25 Decision Tree Examples
26 Random Forests
27 CASE STUDY Decision Trees
28 Intro to Logistic Regression
29 Logistic Regression Example
30 False Positives vs. False Negatives
31 Logistic Regression Equation
32 The Likelihood Function
33 Multivariate Logistic Regression
34 CASE STUDY Logistic Regression
35 Intro to Sentiment Analysis
36 Cleaning Text Data
37 Bag of Words Analysis
38 CASE STUDY Sentiment Analysis

Model Selection & Tuning
39 Intro to Selection & Tuning
40 Hyperparameters
41 Imbalanced Classes
42 Confusion Matrix
43 Accuracy, Precision & Recall
44 Multi-class Confusion Matrix
45 Multi-class Scoring
46 Model Selection
47 Model Drift

Wrapping Up
48 Looking Ahead to Part 3