Data Science In Python: Unsupervised Learning

Learn Python for Data Science & Machine Learning, and build unsupervised learning models with fun, hands-on projects

What you'll learn

Master the foundations of unsupervised Machine Learning in Python, including clustering, anomaly detection, dimensionality reduction, and recommenders

Prepare data for modeling by applying feature engineering, selection, and scaling

Fit, tune, and interpret three types of clustering algorithms: K-Means Clustering, Hierarchical Clustering, and DBSCAN

Use unsupervised learning techniques like Isolation Forests and DBSCAN for anomaly detection

Apply and interpret two types of dimensionality reduction models: Principal Component Analysis (PCA) and t-SNE

Build recommendation engines using content-based and collaborative filtering techniques, including Cosine Similarity and Singular Value Decomposition (SVD)

Requirements

We strongly recommend taking our Data Prep & EDA course before this one

Jupyter Notebooks (free download, we'll walk through the install)

Familiarity with base Python and Pandas is recommended, but not required

Description

This is a hands-on, project-based course designed to help you master the foundations for unsupervised learning in Python.We’ll start by reviewing the data science workflow, discussing the techniques & applications of unsupervised learning, and walking through the data prep steps required for modeling. You’ll learn how to set the correct row granularity for modeling, apply feature engineering techniques, select relevant features, and scale your data using normalization and standardization.From there we'll fit, tune, and interpret 3 popular clustering models using scikit-learn. We’ll start with K-Means Clustering, learn to interpret the output’s cluster centers, and use inertia plots to select the right number of clusters. Next, we’ll cover Hierarchical Clustering, where we’ll use dendrograms to identify clusters and cluster maps to interpret them. Finally, we’ll use DBSCAN to detect clusters and noise points and evaluate the models using their silhouette score.We’ll also use DBSCAN and Isolation Forests for anomaly detection, a common application of unsupervised learning models for identifying outliers and anomalous patterns. You’ll learn to tune and interpret the results of each model and visualize the anomalies using pair plots.Next, we’ll introduce the concept of dimensionality reduction, discuss its benefits for data science, and explore the stages in the data science workflow in which it can be applied. We’ll then cover two popular techniques: Principal Component Analysis, which is great for both feature extraction and data visualization, and t-SNE, which is ideal for data visualization.Last but not least, we’ll introduce recommendation engines, and you'll practice creating both content-based and collaborative filtering recommenders using techniques such as Cosine Similarity and Singular Value Decomposition.Throughout the course you'll play the role of an Associate Data Scientist for the HR Analytics team at a software company trying to increase employee retention. Using the skills you learn throughout the course, you'll use Python to segment the employees, visualize the clusters, and recommend next steps to increase retention.COURSE OUTLINE:Intro to Data ScienceIntroduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflowUnsupervised Learning 101Review the basics of unsupervised learning, including key concepts, types of techniques and applications, and its place in the data science workflowPre-Modeling Data PrepRecap the data prep steps required to apply unsupervised learning models, including restructuring data, engineering & scaling features, and moreClusteringApply three different clustering techniques in Python and learn to interpret their results using metrics, visualizations, and domain expertiseAnomaly DetectionUnderstand where anomaly detection fits in the data science workflow, and apply techniques like Isolation Forests and DBSCAN in PythonDimensionality ReductionUse techniques like Principal Component Analysis (PCA) and t-SNE in Python to reduce the number of features in a data set without losing informationRecommendersRecognize the variety of approaches for creating recommenders, then apply unsupervised learning techniques in Python, including Cosine Similarity and Singular Vector Decomposition (SVD)__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:16.5 hours of high-quality video22 homework assignments7 quizzes3 projectsData Science in Python: Unsupervised Learning ebook (350+ pages)Downloadable project files & solutionsExpert support and Q&A forum30-day Udemy satisfaction guaranteeIf you're an aspiring or seasoned data scientist looking for a practical overview of unsupervised learning techniques in Python with a focus on interpretation, this is the course for you.Happy learning!-Alice Zhao (Python Expert & Data Science Instructor, Maven Analytics)

Overview

Section 1: Getting Started

Lecture 1 Course Introduction

Lecture 2 About This Series

Lecture 3 Course Structure & Outline

Lecture 4 READ ME: Important Notes for New Students

Lecture 5 DOWNLOAD: Course Resources

Lecture 6 Introducing the Course Project

Lecture 7 Setting Expectations

Lecture 8 Jupyter Installation & Launch

Section 2: Intro to Data Science

Lecture 9 Section Introduction

Lecture 10 What is Data Science?

Lecture 11 Data Science Skill Set

Lecture 12 What is Machine Learning?

Lecture 13 Common Machine Learning Algorithms

Lecture 14 Data Science Workflow

Lecture 15 Step 1: Scoping a Project

Lecture 16 Step 2: Gathering Data

Lecture 17 Step 3: Cleaning Data

Lecture 18 Step 4: Exploring Data

Lecture 19 Step 5: Modeling Data

Lecture 20 Step 6: Sharing Insights

Lecture 21 Unsupervised Learning

Lecture 22 Key Takeaways

Section 3: Unsupervised Learning 101

Lecture 23 Section Introduction

Lecture 24 Unsupervised Learning 101

Lecture 25 Unsupervised Learning Techniques

Lecture 26 Unsupervised Learning Applications

Lecture 27 Structure of This Course

Lecture 28 Unsupervised Learning Workflow

Lecture 29 Key Takeaways

Section 4: Pre-Modeling Data Prep

Lecture 30 Section Introduction

Lecture 31 Data Prep for Unsupervised Learning

Lecture 32 Setting the Correct Row Granularity

Lecture 33 DEMO: Group By

Lecture 34 DEMO: Pivot

Lecture 35 ASSIGNMENT: Setting the Correct Row Granularity

Lecture 36 SOLUTION: Setting the Correct Row Granularity

Lecture 37 Preparing Columns for Modeling

Lecture 38 Identifying Missing Data

Lecture 39 Handling Missing Data

Lecture 40 Converting to Numeric

Lecture 41 Converting to DateTime

Lecture 42 Extracting DateTime

Lecture 43 Calculating Based on a Condition

Lecture 44 Dummy Variables

Lecture 45 ASSIGNMENT: Preparing Columns for Modeling

Lecture 46 SOLUTION: Preparing Columns for Modeling

Lecture 47 Feature Engineering

Lecture 48 Feature Engineering During Data Prep

Lecture 49 Applying Calculations

Lecture 50 Binning Values

Lecture 51 Identifying Proxy Variables

Lecture 52 Feature Engineering Tips

Lecture 53 ASSIGNMENT: Feature Engineering

Lecture 54 SOLUTION: Feature Engineering

Lecture 55 Excluding Identifiers From Modeling

Lecture 56 Feature Selection

Lecture 57 ASSIGNMENT: Feature Selection

Lecture 58 SOLUTION: Feature Selection

Lecture 59 Feature Scaling

Lecture 60 Normalization

Lecture 61 Standardization

Lecture 62 ASSIGNMENT: Feature Scaling

Lecture 63 SOLUTION: Feature Scaling

Lecture 64 Key Takeaways

Section 5: Clustering

Lecture 65 Section Introduction

Lecture 66 Clustering Basics

Lecture 67 K-Means Clustering

Lecture 68 K-Means Clustering in Python

Lecture 69 DEMO: K-Means Clustering in Python

Lecture 70 Visualizing K-Means Clustering

Lecture 71 Interpreting K-Means Clustering

Lecture 72 Visualizing Cluster Centers

Lecture 73 ASSIGNMENT: K-Means Clustering

Lecture 74 SOLUTION: K-Means Clustering

Lecture 75 Inertia

Lecture 76 Plotting Inertia in Python

Lecture 77 DEMO: Plotting Inertia in Python

Lecture 78 ASSIGNMENT: Inertia Plot

Lecture 79 SOLUTION: Inertia Plot

Lecture 80 Tuning a K-Means Model

Lecture 81 DEMO: Tuning a K-Means Model

Lecture 82 ASSIGNMENT: Tuning a K-Means Model

Lecture 83 SOLUTION: Tuning a K-Means Model

Lecture 84 Selecting the Best Model

Lecture 85 DEMO: Selecting the Best Model

Lecture 86 ASSIGNMENT: Selecting the Best K-Means Model

Lecture 87 SOLUTION: Selecting the Best K-Means Model

Lecture 88 Hierarchical Clustering

Lecture 89 Dendrograms in Python

Lecture 90 Agglomerative Clustering in Python

Lecture 91 DEMO: Agglomerative Clustering in Python

Lecture 92 Cluster Maps in Python

Lecture 93 DEMO: Cluster Maps in Python

Lecture 94 ASSIGNMENT: Hierarchical Clustering

Lecture 95 SOLUTION: Hierarchical Clustering

Lecture 96 DBSCAN

Lecture 97 DBSCAN in Python

Lecture 98 Silhouette Score

Lecture 99 Silhouette Score in Python

Lecture 100 DEMO: DBSCAN and Silhouette Score in Python

Lecture 101 ASSIGNMENT: DBSCAN

Lecture 102 SOLUTION: DBSCAN

Lecture 103 Comparing Clustering Algorithms

Lecture 104 Clustering Next Steps

Lecture 105 DEMO: Compare Clustering Models

Lecture 106 DEMO: Label Unseen Data

Lecture 107 Key Takeaways

Section 6: PROJECT: Clustering Clients

Lecture 108 Project Overview

Lecture 109 SOLUTION: Data Prep

Lecture 110 SOLUTION: K-Means Clustering

Lecture 111 SOLUTION: Hierarchical Clustering

Lecture 112 SOLUTION: DBSCAN

Lecture 113 SOLUTION: Compare, Recommend and Predict

Section 7: Anomaly Detection

Lecture 114 Section Introduction

Lecture 115 Anomaly Detection Basics

Lecture 116 Anomaly Detection Approaches

Lecture 117 Anomaly Detection Workflow

Lecture 118 Isolation Forests

Lecture 119 Isolation Forests in Python

Lecture 120 Visualizing Anomalies

Lecture 121 Tuning and Interpreting Isolation Forests

Lecture 122 ASSIGNMENT: Isolation Forests

Lecture 123 SOLUTION: Isolation Forests

Lecture 124 DBSCAN for Anomaly Detection

Lecture 125 DBSCAN for Anomaly Detection in Python

Lecture 126 Visualizing DBSCAN Anomalies

Lecture 127 ASSIGNMENT: DBSCAN for Anomaly Detection

Lecture 128 SOLUTION: DBSCAN for Anomaly Detection

Lecture 129 Comparing Anomaly Detection Algorithms

Lecture 130 RECAP: Clustering and Anomaly Detection

Lecture 131 Key Takeaways

Section 8: Dimensionality Reduction

Lecture 132 Section Introduction

Lecture 133 Dimensionality Reduction Basics

Lecture 134 Why Reduce Dimensions?

Lecture 135 Dimensionality Reduction Workflow

Lecture 136 Principal Component Analysis

Lecture 137 Principal Component Analysis in Python

Lecture 138 Explained Variance Ratio

Lecture 139 DEMO: PCA and Explained Variance Ratio in Python

Lecture 140 ASSIGNMENT: Principal Component Analysis

Lecture 141 SOLUTION: Principal Component Analysis

Lecture 142 Interpreting PCA

Lecture 143 DEMO: Interpreting PCA

Lecture 144 ASSIGNMENT: Interpreting PCA

Lecture 145 SOLUTION: Interpreting PCA

Lecture 146 Feature Selection vs Feature Extraction

Lecture 147 PCA Next Steps

Lecture 148 T-SNE

Lecture 149 T-SNE in Python

Lecture 150 ASSIGNMENT: T-SNE

Lecture 151 SOLUTION: T-SNE

Lecture 152 PCA vs t-SNE

Lecture 153 DEMO: Dimensionality Reduction and Clustering

Lecture 154 ASSIGNMENT: T-SNE & K-Means Clustering

Lecture 155 SOLUTION: T-SNE & K-Means Clustering

Lecture 156 Key Takeaways

Section 9: Recommenders

Lecture 157 Section Introduction

Lecture 158 Recommenders Basics

Lecture 159 Content-Based Filtering

Lecture 160 Cosine Similarity

Lecture 161 Cosine Similarity in Python

Lecture 162 Making a Content Based Filtering Recommendation

Lecture 163 ASSIGNMENT: Content-Based Filtering

Lecture 164 SOLUTION: Content-Based Filtering

Lecture 165 Collaborative Filtering

Lecture 166 User-Item Matrix

Lecture 167 ASSIGNMENT: User-Item Matrix

Lecture 168 SOLUTION: User-Item Matrix

Lecture 169 Singular Value Decomposition

Lecture 170 Singular Value Decomposition in Python

Lecture 171 ASSIGNMENT: Singular Value Decomposition

Lecture 172 SOLUTION: Singular Value Decomposition

Lecture 173 Choosing the Number of Components

Lecture 174 DEMO: Choosing the Number of Components

Lecture 175 ASSIGNMENT: Choosing the Number of Components

Lecture 176 SOLUTION: Choosing the Number of Components

Lecture 177 Making a Collaborative Filtering Recommendation

Lecture 178 DEMO: Making a Collaborative Filtering Recommendation

Lecture 179 ASSIGNMENT: Collaborative Filtering

Lecture 180 SOLUTION: Collaborative Filtering

Lecture 181 Recommender Next Steps

Lecture 182 DEMO: Hybrid Approach

Lecture 183 Key Takeaways

Section 10: PROJECT: Recommending Restaurants

Lecture 184 Project Overview

Lecture 185 SOLUTION: Data Prep

Lecture 186 SOLUTION: TruncatedSVD

Lecture 187 SOLUTION: Cosine Similarity

Lecture 188 SOLUTION: Recommendations

Section 11: Unsupervised Learning Review

Lecture 189 Section Introduction

Lecture 190 Unsupervised Learning Flow Chart

Lecture 191 Unsupervised Learning Techniques & Applications

Lecture 192 Unsupervised Learning in the Data Science Workflow

Lecture 193 Key Takeaways

Section 12: Final Project

Lecture 194 Final Project Overview

Lecture 195 SOLUTION: Data Prep & EDA

Lecture 196 SOLUTION: Clustering

Lecture 197 SOLUTION: PCA

Lecture 198 SOLUTION: Clustering (Round 2)

Lecture 199 SOLUTION: PCA (Round 2)

Lecture 200 SOLUTION: EDA on Clusters

Lecture 201 SOLUTION: Recommendations

Section 13: Next Steps

Lecture 202 BONUS LESSON

Data scientists who want to learn how to build and interpret unsupervised learning models in Python,Analysts or BI experts looking to learn about unsupervised learning or transition into a data science role,Anyone interested in learning one of the most popular open source programming languages in the world