English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 182 lectures (8h 51m) | 3.20 GB
Explore Data Science & Machine Learning topics with simple, step-by-step demos and user-friendly Excel models (NO code!)
This course is for everyday people looking for an intuitive, beginner-friendly introduction to the world of machine learning and data science.
Build confidence with guided, step-by-step demos, and learn foundational skills from the ground up. Instead of memorizing complex math or learning a new coding language, we’ll break down and explore machine learning techniques to help you understand exactly how and why they work.
Follow along with simple, visual examples and interact with user-friendly, Excel-based models to learn topics like linear and logistic regression, decision trees, KNN, naïve bayes, hierarchical clustering, sentiment analysis, and more – without writing a SINGLE LINE of code.
This course combines 4 best-selling courses from Maven Analytics into a single masterclass:
PART 1: Univariate & Multivariate Profiling
PART 2: Classification Modeling
PART 3: Regression & Forecasting
PART 4: Unsupervised Learning
PART 1: Univariate & Multivariate Profiling
In Part 1 we’ll introduce the machine learning workflow and common techniques for cleaning and preparing raw data for analysis. We’ll explore univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation:
Section 1: Machine Learning Intro & Landscape
Machine learning process, definition, and landscape
Section 2: Preliminary Data QA
Variable types, empty values, range & count calculations, left/right censoring, etc.
Section 3: Univariate Profiling
Histograms, frequency tables, mean, median, mode, variance, skewness, etc.
Section 4: Multivariate Profiling
Violin & box plots, kernel densities, heat maps, correlation, etc.
Throughout the course, we’ll introduce real-world scenarios to solidify key concepts and simulate actual data science and business intelligence cases. You’ll use profiling metrics to clean up product inventory data for a local grocery, explore Olympic athlete demographics with histograms and kernel densities, visualize traffic accident frequency with heat maps, and more.
PART 2: Classification Modeling
In Part 2 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 like 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 & classification workflow, feature engineering, 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 drift
You’ll help build a simple recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for an online travel company, extract sentiment from a sample of book reviews, and more.
PART 3: Regression & Forecasting
In Part 3 we’ll introduce core building blocks like linear relationships and least squared error, and practice applying them to univariate, multivariate, and non-linear regression models. We’ll review diagnostic metrics like R-squared, mean error, F-significance, and P-Values, then use time-series forecasting techniques to identify seasonality, predict nonlinear trends, and measure the impact of key business decisions using intervention analysis:
Section 1: Intro to Regression
Supervised learning landscape, regression vs. classification, prediction vs. root-cause analysis
Section 2: Regression Modeling 101
Linear relationships, least squared error, univariate & multivariate regression, nonlinear transformation
Section 3: Model Diagnostics
R-squared, mean error, null hypothesis, F-significance, T & P-values, homoskedasticity, multicollinearity
Section 4: Time-Series Forecasting
Seasonality, auto correlation, linear trending, non-linear models, intervention analysis
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.
PART 4: Unsupervised Learning
In Part 4 we’ll explore the differences between supervised and unsupervised machine learning and introduce several common unsupervised techniques, including cluster analysis, association mining, outlier detection and dimensionality reduction. We’ll break down each model in simple terms and help you build an intuition for how they work, from K-means and apriori to outlier detection, principal component analysis, and more:
Section 1: Intro to Unsupervised Machine Learning
Unsupervised learning landscape & workflow, common unsupervised techniques, feature engineering
Section 2: Clustering & Segmentation
Clustering basics, K-means, elbow plots, hierarchical clustering, dendograms
Section 3: Association Mining
Association mining basics, apriori, basket analysis, minimum support thresholds, markov chains
Section 4: Outlier Detection
Outlier detection basics, cross-sectional outliers, nearest neighbors, time-series outliers, residual distribution
Section 5: Dimensionality Reduction
Dimensionality reduction basics, principle component analysis (PCA), scree plots, advanced techniques
You’ll see how K-means can help identify customer segments, how apriori can be used for basket analysis and recommendation engines, and how outlier detection can spot anomalies in cross-sectional or time-series datasets.
What you’ll learn
- Build foundational machine learning & data science skills WITHOUT writing complex code
- Play with interactive, user-friendly Excel models to learn how machine learning techniques actually work
- Enrich datasets using feature engineering techniques like one-hot encoding, scaling and discretization
- Predict categorical outcomes using classification models like K-nearest neighbors, naïve bayes, and decision trees
- Build accurate forecasts and projections using linear and non-linear regression models
- Apply powerful techniques for clustering, association mining, outlier detection, and dimensionality reduction
- Learn how to select and tune models to optimize performance, reduce bias, and minimize drift
- Explore unique, hands-on case studies to simulate how machine learning can be applied to real-world cases
Table of Contents
Getting Started
1 Course Structure Outline
2 READ ME Important Notes for New Students
3 DOWNLOAD Course Resources
4 Setting Expectations
PART 1 QA Data Profiling
5 Part 1 QA Data Profiling
Intro to the ML Landscape
6 Intro to Machine Learning
7 When is ML the right fit
8 The Machine Learning Process
9 The Machine Learning Landscape
Preliminary Data QA
10 Introduction
11 Why QA
12 Variable Types
13 Empty Values
14 Range Calculations
15 Count Calculations
16 Left Right Censored Data
17 Table Structure
18 CASE STUDY Preliminary QA
19 BEST PRACTICES Preliminary QA
Univariate Profiling
20 Introduction
21 Categorical Variables
22 Discretization
23 Nominal vs Ordinal
24 Categorical Distributions
25 Numerical Variables
26 Histograms Kernel Densities
27 CASE STUDY Histograms
28 Normal Distribution
29 CASE STUDY Normal Distribution
30 Univariate Data Profiling
31 Mode
32 Mean
33 Median
34 Percentile
35 Variance
36 Standard Deviation
37 Skewness
38 BEST PRACTICES Univariate Profiling
Multivariate Profiling
39 Introduction
40 CategoricalCategorical
41 CASE STUDY Heat Maps
42 CategoricalNumerical
43 Multivariate Kernel Densities
44 Violin Plots
45 Box Plots
46 Limitations of Categorical Distributions
47 NumericalNumerical
48 Correlation
49 Correlation vs Causation
50 Visualizing Third Dimension
51 CASE STUDY Correlation
52 BEST PRACTICES Multivariate Profiling
53 Looking Ahead to Part 2
PART 2 Classification Modeling
54 Part 2 Classification Modeling
Intro to Classification
55 Supervised vs Unsupervised Learning
56 Classification vs Regression
57 RECAP Key Concepts
58 Classification 101
59 Classification Workflow
60 Feature Engineering
61 Data Splitting
62 Overfitting
Classification Models
63 Common Classification Models
64 Intro to KNearest Neighbors KNN
65 KNN Examples
66 CASE STUDY KNN
67 Intro to Naive Bayes
68 Naive Bayes Frequency Tables
69 Naive Bayes Conditional Probability
70 CASE STUDY Naive Bayes
71 Intro to Decision Trees
72 Decision Trees Entropy 101
73 Entropy Information Gain
74 Decision Tree Examples
75 Random Forests
76 CASE STUDY Decision Trees
77 Intro to Logistic Regression
78 Logistic Regression Example
79 False Positives vs False Negatives
80 Logistic Regression Equation
81 The Likelihood Function
82 Multivariate Logistic Regression
83 CASE STUDY Logistic Regression
84 Intro to Sentiment Analysis
85 Cleaning Text Data
86 Bag of Words Analysis
87 CASE STUDY Sentiment Analysis
Model Selection Tuning
88 Intro to Selection Tuning
89 Hyperparameters
90 Imbalanced Classes
91 Confusion Matrix
92 Accuracy Precision Recall
93 Multiclass Confusion Matrix
94 Multiclass Scoring
95 Model Selection
96 Model Drift
97 Looking ahead to Part 3
PART 3 Regression Forecasting
98 Part 3 Regression Forecasting
Intro to Regression
99 Supervised vs Unsupervised Learning
100 RECAP Key Concepts
101 Regression 101
102 Feature Engineering for Regression
103 Prediction vs RootCause Analysis
Regression Modeling 101
104 Intro to Regression Modeling
105 Linear Relationships
106 Least Squared Error
107 Univariate Linear Regression
108 CASE STUDY Univariate Linear Regression
109 Multiple Linear Regression
110 NonLinear Regression
111 CASE STUDY NonLinear Regression
Model Diagnostics
112 Intro to Model Diagnostics
113 Sample Model Output
114 RSquared
115 Mean Error Metrics MSE MAE MAPE
116 Homoskedasticity
117 Null Hypothesis
118 FSignificance
119 TValues PValues
120 Multicollinearity
121 Variance Inflation Factor
122 RECAP Sample Model Output
TimeSeries Forecasting
123 Intro to Forecasting
124 Seasonality
125 Auto Correlation Function
126 CASE STUDY Seasonality with ACF
127 OneHot Encoding
128 CASE STUDY Seasonality with OneHot Encoding
129 Linear Trending
130 CASE STUDY Seasonality with Linear Trend
131 Smoothing
132 CASE STUDY Smoothing
133 NonLinear Trends
134 CASE STUDY NonLinear Trend
135 Intervention Analysis
136 CASE STUDY Intervention Analysis
137 Looking Ahead to Part 4
PART 4 Unsupervised Learning
138 Part 4 Unsupervised Learning
Intro to Unsupervised ML
139 Supervised vs Unsupervised Learning
140 Common Unsupervised Techniques
141 Unsupervised ML Workflow
142 RECAP Feature Engineering
143 KEY TAKEAWAYS Intro to Unsupervised ML
Clustering Segmentation
144 Introduction
145 Clustering Basics
146 Intro to KMeans
147 WSS Elbow Plots
148 KMeans FAQs
149 CASE STUDY KMeans
150 Intro to Hierarchical Clustering
151 Anatomy of a Dendrogram
152 Hierarchical Clustering FAQs
153 KEY TAKEAWAYS Clustering Segmentation
Association Mining Basket Analysis
154 Introduction
155 Association Mining Basics
156 The Apriori Algorithm
157 Basket Analysis Examples
158 Minimum Support Thresholds
159 Infrequent Itemsets
160 Multiple Item Sets
161 CASE STUDY Apriori
162 Markov Chains
163 CASE STUDY Markov Chains
164 KEY TAKEAWAYS Association Mining
Outlier Detection
165 Introduction
166 Outlier Detection Basics
167 CrossSectional Outliers
168 CrossSectional Outlier Example
169 CASE STUDY CrossSectional Outlier
170 TimeSeries Outliers
171 TimeSeries Outlier Example
172 KEY TAKEAWAYS Outlier Detection
Dimensionality Reduction
173 Introduction
174 Dimensionality Reduction Basics
175 Principle Component Analysis
176 PCA Example
177 Interpreting Components
178 Scree Plots
179 Advanced Techniques
180 KEY TAKEAWAYS Dimensionality Reduction
Wrapping Up
181 Series Conclusion
182 BONUS LESSON
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