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DATA SCIENCE INFINITY
Welcome - let's get you set up for success!
Welcome to DATA SCIENCE INFINITY! (2:10)
Join the private Slack channel - and start getting *dedicated* support & guidance
How to get the most out of the private Slack channel
What makes a GREAT Data Scientist? (2:41)
Self Confidence & Imposter Syndrome (3:17)
Course Overview (3:23)
Your DSI Downloadable Resources Library
SQL for Data Science
Introduction to SQL for Data Science (8:49)
Connecting to the DATA SCIENCE INFINITY cloud database (PRACTICAL) (10:06)
Introduction to SQL Workbench/J (6:58)
SQL Workbench/J Troubleshooting 1 - Connection Issues
SQL Workbench/J Troubleshooting 2 - Frequent Disconnection From Database
The SELECT statement (PRACTICAL) (9:43)
The SELECT Statement (Test Yourself)
Applying selection conditions using the WHERE statement (PRACTICAL) (8:48)
The WHERE Statement (Test Yourself)
Aggregation functions and the GROUP BY statement (PRACTICAL) (10:11)
Aggregation functions and the GROUP BY statement (Test Yourself)
Conditional rules using CASE WHEN (PRACTICAL) (9:12)
Conditional rules using CASE WHEN (Test Yourself)
The use of WINDOW functions (PRACTICAL) (11:38)
The use of WINDOW functions (Test Yourself)
Joining tables using JOIN (PRACTICAL) (19:23)
Joining tables using JOIN (Test Yourself)
The only SQL Joins Cheatsheet you'll ever need (image)
Stacking data using UNION and UNION ALL (PRACTICAL) (4:34)
Executing multiple queries using TEMP TABLES and CTE (9:50)
Executing multiple queries using TEMP TABLES and CTE (Test Yourself)
Other useful TIPS & TRICKS! (PRACTICAL) (20:50)
Real World SQL Test 01 - Questions
Real World SQL Test 01 - Solutions (13:47)
Real World SQL Test 02 - Questions
Real World SQL Test 02 - Solutions (15:53)
Real World SQL Test 03 - Questions
Real World SQL Test 03 - Solutions (16:24)
Downloadable PDF: 10 Common SQL Theory Questions (With Answers)
Data Viz With Tableau!
Introducing Tableau (and what we'll learn!) (2:31)
Get your hands on the data!
Installing Tableau (6:02)
Importing Data Into Tableau (8:51)
Our First Data Visualisation! (15:21)
Measures, Dimensions, and Marks (10:23)
Filters (10:01)
Pages (7:24)
Let's Create Our Next Viz (Part 1) (14:19)
Let's Create Our Next Viz (Part 2) (10:52)
Calculated Fields (Part 1) (12:03)
Calculated Fields (Part 2) (10:47)
Calculated Fields (Part 3) (8:32)
Dashboard Requirements From Our Client! (2:34)
The Top 10 Earthquakes Table (7:17)
Percent Of Earthquakes By Location (5:20)
Creating Our Amazing Dashboard (Part 1) (11:48)
Creating Our Amazing Dashboard (Part 2) (7:52)
Creating Our Amazing Dashboard (Part 3) (3:24)
Download The Logo Image
Creating Our Amazing Dashboard (Part 4) (7:52)
Python for Data Science 01: Base Python
Introduction to Python for Data Science (8:56)
Installing Anaconda (PRACTICAL) (7:21)
Introduction to Spyder (PRACTICAL) (5:09)
Introducing VARIABLES and DATA TYPES (PRACTICAL) (10:04)
Assigning our data to VARIABLES (PRACTICAL) (4:50)
A deeper look at working with STRINGS (PRACTICAL) (16:19)
A deeper look at working with NUMBERS (PRACTICAL) (7:02)
Introduction to DATA STRUCTURES (PRACTICAL) (1:06)
Data Structure 1: LISTS (PRACTICAL) (17:41)
Data Structure 2: TUPLES (PRACTICAL) (7:04)
Data Structure 3: SETS (PRACTICAL) (10:57)
Data Structure 4: DICTIONARIES (PRACTICAL) (11:28)
Adding smarts to our code using CONDITIONAL STATEMENTS (PRACTICAL) (13:03)
Going loopy with FOR LOOPS (PRACTICAL) (12:57)
Loop de Loop with WHILE LOOPS (PRACTICAL) (5:59)
Receiving information using the INPUT FUNCTION (PRACTICAL) (5:30)
** MINI PROJECT ** Building a Number Guessing Game (PRACTICAL) (13:28)
Getting func'y with FUNCTIONS (PRACTICAL) (8:40)
** MINI PROJECT ** Finding Prime Numbers (PRACTICAL) (22:34)
A note on using pop() with sets in Python
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Get to know the very useful LIST COMPREHENSION (PRACTICAL) (9:04)
Handling Exceptions with....EXCEPTION HANDLING (PRACTICAL) (9:00)
Where to from here... (2:15)
Python for Data Science 02: Numpy
Introduction to Numpy (3:24)
Creating Numpy Arrays (PRACTICAL) (15:02)
Numpy Array Operations (PRACTICAL) (11:46)
Manipulating Numpy Arrays (PRACTICAL) (14:11)
** MINI PROJECT ** Calculating Planet Volumes (PRACTICAL) (10:31)
** MINI PROJECT ** Image Manipulation using Numpy (Get the data)
** MINI PROJECT ** Image Manipulation (PRACTICAL) (17:06)
Python for Data Science 03: Pandas
Introduction To Pandas (1:36)
Accessing & Downloading The Data
Creating Pandas DataFrames & Importing Data (PRACTICAL) (11:50)
Exploring & Understanding DataFrame Data (PRACTICAL) (14:23)
Accessing Specific Columns In Our DataFrame (PRACTICAL) (7:51)
Adding & Dropping Columns In Our DataFrame (PRACTICAL) (9:34)
Adding Columns Using Map, Replace, And Apply (PRACTICAL) (13:54)
Sorting & Ranking Data (PRACTICAL) (12:40)
Selecting Rows & Columns using LOC & ILOC (PRACTICAL) (19:35)
Renaming Columns (PRACTICAL) (8:43)
Joining & Merging DataFrames (PRACTICAL) (14:48)
Aggregating Data Using GROUPBY (PRACTICAL) (18:58)
Pivoting A DataFrame (PRACTICAL) (11:33)
Dealing With Missing Values (PRACTICAL) (21:32)
Dealing With Duplicate Data (PRACTICAL) (9:05)
Creating Charts And Plots Using Pandas (PRACTICAL) (15:52)
Exporting Data (PRACTICAL) (14:23)
Python for Data Science 04: Matplotlib
Introduction To Matplotlib (1:22)
Our First Plot (PRACTICAL) (8:56)
Formatting Our Plot: Features (PRACTICAL) (9:00)
Formatting Our Plot: Colours & Styles (PRACTICAL) (13:52)
Working With Subplots (PRACTICAL) (8:26)
Let's Grab Some Height & Weight Data To Use!
Creating & Refining A Histogram (PRACTICAL) (9:23)
Creating & Refining A Scatter Plot (PRACTICAL) (8:44)
Enhancing Our Plots Using Visual Aids (PRACTICAL) (8:03)
Adding Text To Our Plots (PRACTICAL) (17:08)
Saving Plots (PRACTICAL) (3:33)
An intuitive & fun adventure through Statistics for Data Science!
Section Introduction (1:07)
The Different "Types" Of Data (5:23)
Quiz Time! Statistics - Types Of Data
Understanding Distributions & Standard Deviation (14:50)
Normal Distributions + Z-Scores (6:15)
The 6 Distributions You Need To Know! (9:24)
Quiz Time! Statistics - Distributions
The Central Limit Theorem (9:36)
Getting Confident With Confidence Intervals (9:32)
Quiz Time! Statistics - Dealing With Uncertainty
Introduction To Hypothesis Testing & P-Values (12:41)
Hypothesis Testing: The One Sample T-Test (13:47)
Hypothesis Testing: The Independent Samples T-Test (14:02)
Hypothesis Testing: The Paired T-Test (9:43)
Hypothesis Testing: The Chi-Square Test Of Independence (12:09)
Note: Chi-Square Test vs. Z-Test For Proportions
Quiz Time! Statistics - Hypothesis Testing & P-Values
Jupyter Notebook for Data Science
An overview of Jupyter Notebook (12:53)
Github for Data Science
Overview of Git & Github (9:03)
Getting Started - Branches, Pull Requests, and Merges! (PRACTICAL) (17:45)
Forking a Repository (PRACTICAL) (6:27)
Pushing and Pulling between your local PC and GitHub (PRACTICAL) (13:33)
Downloadable PDF: The Essential Github Lingo for Data Science
Introducing ABC Grocery - our client in desperate need of Data Science expertise!
Introducing ABC Grocery! (5:01)
Getting The Data
AB Testing - Theory & Application
What is AB Testing? (2:28)
Our Task for ABC Grocery! (2:09)
Getting The Data
Chi-Square Test for ABC Grocery (PRACTICAL) (18:32)
Add the pre-built project to your portfolio!
Let's code up a One Sample T-Test (10:18)
Let's code up an Independent Samples T-Test (9:02)
Let's code up a Paired Sample T-Test (7:50)
Introduction to Machine Learning
Introduction to Machine Learning in Data Science (16:52)
Overview of Supervised Learning (13:12)
Overview of Unsupervised Learning (7:49)
Introducing scikit-learn for Machine Learning in Python (9:38)
Preparing & Cleaning Data for Machine Learning
Data Cleaning & Prep For ML - Introduction (1:01)
A Checklist for Data Cleaning & Preparation (7:17)
Dealing with Missing Values (THEORY) (8:47)
Dealing with Missing Values - Pandas (PRACTICAL) (12:42)
Dealing with Missing Values - SimpleImputer (PRACTICAL) (11:05)
Dealing with Missing Values - KNNImputer (PRACTICAL) (11:49)
Dealing with Categorical Variables (THEORY) (8:18)
Dealing with Categorical Variables - One Hot Encoder (PRACTICAL) (10:50)
Dealing with Outliers (THEORY) (8:55)
Dealing with Outliers (PRACTICAL) (13:34)
Feature Scaling for Machine Learning (THEORY) (9:19)
Feature Scaling for Machine Learning (PRACTICAL) (8:18)
Feature Selection in Machine Learning (THEORY) (12:04)
Feature Selection in Machine Learning - Getting the Sample Data
Feature Selection in Machine Learning - Correlation Matrix (PRACTICAL) (4:26)
Feature Selection in Machine Learning - Univariate Testing (PRACTICAL) (17:52)
Feature Selection in Machine Learning - RFECV (PRACTICAL) (13:48)
Model Validation & Over-fitting (THEORY) (8:53)
Model Validation & Over-fitting (PRACTICAL) (18:06)
Quiz Time! ML Data Cleaning & Preparation
Machine Learning for Regression Tasks
Introduction to Machine Learning for Regression (4:02)
Our Regression Task for ABC Grocery (2:39)
Our Regression Task for ABC Grocery - Getting The Data
Our Regression Task for ABC Grocery - Creating The Data (16:16)
Getting The Sample Data
Linear Regression
High Level Overview (5:09)
Basic Code Stencil (PRACTICAL) (8:57)
The Formula for a Straight Line (ADVANCED THEORY) (9:00)
Finding the "best" line using Least Squares (ADVANCED THEORY) (12:57)
Evaluating Model Fit using R-Squared (ADVANCED THEORY) (11:32)
Multiple Input Variables (ADVANCED THEORY) (4:51)
Adjusted R-Squared (ADVANCED THEORY) (5:40)
Understanding P-Values (ADVANCED THEORY) (6:33)
Advanced Code Template (PRACTICAL) (29:31)
Quiz Time! Linear Regression Concepts
Decision Trees for Regression (Regression Trees)
High Level Overview (10:40)
Basic Code Stencil (PRACTICAL) (10:03)
Splitting Criteria (ADVANCED THEORY) (13:46)
Stopping Criteria (ADVANCED THEORY) (4:04)
Evaluating Model Performance (ADVANCED THEORY) (4:06)
Advanced Code Template (PRACTICAL) (21:20)
Quiz Time! Regression Tree Concepts
Random Forests for Regression
High Level Overview (9:58)
Basic Code Stencil (PRACTICAL) (9:16)
Feature Importance (ADVANCED THEORY) (5:40)
Evaluating Model Performance (ADVANCED THEORY) (4:15)
Advanced Code Template (PRACTICAL) (22:33)
Predicting The Missing Loyalty Scores (PRACTICAL) (8:53)
Add the pre-built project to your portfolio!
Quiz Time! Random Forest (Regression) Concepts
Machine Learning for Classification Tasks
Introduction to Machine Learning for Classification (4:26)
Our Task for ABC Grocery (3:56)
Getting the sample data
Logistic Regression
High Level Overview (10:38)
Basic Code Stencil (PRACTICAL) (13:09)
Probability, Odds, and log(Odds) (ADVANCED THEORY) (6:50)
The Formula for a Sigmoid Curve (ADVANCED THEORY) (5:42)
Maximum Likelihood Estimation (ADVANCED THEORY) (8:26)
Evaluating Classification Accuracy (ADVANCED THEORY) (7:09)
Advanced Evaluation Techniques (ADVANCED THEORY) (11:12)
Changing the Classification Threshold (ADVANCED THEORY) (10:32)
Advanced Code Template (PRACTICAL) (30:49)
Quiz Time! Logistic Regression Concepts
Decision Trees for Classification (Classification Trees)
High Level Overview (7:15)
Basic Code Stencil (PRACTICAL) (9:01)
Splitting Criteria (ADVANCED THEORY) (12:05)
Stopping Criteria (ADVANCED THEORY) (4:10)
Evaluating Classification Accuracy (ADVANCED THEORY) (6:31)
Advanced Code Template (PRACTICAL) (16:44)
Quiz Time! Classification Tree Concepts
Random Forests for Classification
High Level Overview (9:17)
Basic Code Stencil (PRACTICAL) (6:35)
Feature Importance (ADVANCED THEORY) (4:40)
Evaluating Classification Accuracy (ADVANCED THEORY) (6:03)
Advanced Code Template (PRACTICAL) (15:40)
Quiz Time! Random Forest (Classification) Concepts
K-Nearest-Neighbours (KNN) for Classification
High Level Overview (5:46)
Basic Code Stencil (PRACTICAL) (6:30)
Measuring Distances In Multi-Dimensional Space (ADVANCED THEORY) (6:16)
The Importance Of Feature Scaling (ADVANCED THEORY) (5:07)
What Value For "K"? (ADVANCED THEORY) (4:26)
Advanced Code Template (PRACTICAL) (22:46)
Add the pre-built project to your portfolio!
Quiz Time! KNN Concepts
Advanced applications of scikit-learn
Grid Search for Hyperparameter Tuning (THEORY) (6:15)
Grid Search for Hyperparameter Tuning (PRACTICAL) (10:41)
Pipelines - getting the sample data
Automating workflows with Pipelines (PRACTICAL) (23:27)
Unsupervised Learning: K-Means Clustering
High Level Overview (7:58)
Getting The Sample Data
Basic Code Stencil (PRACTICAL) (13:02)
Measuring Distances In Multi-Dimensional Space (ADVANCED THEORY) (5:58)
The Importance Of Feature Scaling (ADVANCED THEORY) (5:17)
What Value For "K"? (ADVANCED THEORY) (7:31)
Our Task For ABC Grocery (2:23)
Advanced Code Template (PRACTICAL) (26:18)
Add the pre-built project to your portfolio!
Quiz Time! K-Means Concepts
Unsupervised Learning: Principal Component Analysis (PCA)
High Level Overview (8:26)
Our Task For ABC Grocery (2:30)
Getting The Data
Advanced Code Template (PRACTICAL) (21:02)
Add the pre-built project to your portfolio!
Quiz Time! PCA Concepts
Unsupervised Learning: Association Rule Learning
High Level Overview (12:24)
Our Task For ABC Grocery (1:30)
Getting The Data
Advanced Code Template (PRACTICAL) (21:54)
Add the pre-built project to your portfolio!
Quiz Time! Association Rule Learning
Causal Impact Analysis
High Level Overview (11:24)
Our Task For ABC Grocery (1:36)
Advanced Code Template (PRACTICAL) (18:06)
Add the pre-built project to your portfolio!
Quiz Time! Causal Impact Analysis
Machine Learning Model Deployment
Overview of ML Model Deployment & The Important Considerations We Need To Make! (8:17)
Getting & Setting Up The Required Files For This Section
An Overview Of Streamlit (9:57)
Let's Get Streamlit Installed! (6:35)
Coding Our Web-App Part 1 (PRACTICAL) (15:29)
Coding Our Web-App Part 2 (PRACTICAL) (18:10)
Deploying Our Web-App Part 1 (PRACTICAL) (4:22)
Deploying Our Web-App Part 2 (PRACTICAL) (11:53)
Set yourself apart: The art of turning business problems into Data Science solutions
Introduction (3:39)
Getting to the core of the business problem (9:55)
Deciding on the right Data Science solution (11:15)
From Learning to Earning: A Framework for Success!
Section Introduction (4:17)
The 3 key areas for Learning to Earning in Data Science (5:00)
BRAND - Small changes to make your CV or Resume stand out (8:27)
BRAND - What hiring managers want to see from Projects & Portfolios (6:31)
BRAND - Small changes that can have a big impact (3:16)
APPLYING - Understanding the role (3:49)
APPLYING - Speaking to a Recruiter or HR (3:24)
APPLYING - An overview of Data Science Interviews (3:31)
INTERVIEWING - Keep the human connection in mind: Simple ways to build rapport (2:38)
INTERVIEWING - Effectively answering questions you don't know the answer to... (3:44)
INTERVIEWING - Effectively answering questions about mistakes you've made... (2:33)
INTERVIEWING - Tips for Take-Home Assignments (7:20)
INTERVIEWING - Tips for Coding Tests (6:36)
INTERVIEWING - Questions to ask (and not ask) your interviewer (2:09)
Let's talk about rejections... (2:38)
C.R.A.I.G - your system for tackling the Data Science hiring process!
Systematically Approaching The Hiring Process - Introduction (4:14)
How To Apply The C.R.A.I.G System - And Get Ahead Of The Competition! (13:18)
Your C.R.A.I.G System Help-Sheet (Downloadable PDF)
The DATA SCIENCE INFINITY Data Science Resume Template
Introduction To Deep Learning
Section Overview (3:40)
Deep Learning - Artificial Neural Networks
Introduction To Artificial Neural Networks (20:31)
Weights & Biases (17:15)
Activation Functions (23:16)
Calculating Loss (19:30)
Introduction To Back Propagation & Gradient Descent (18:56)
The Relationship Between Network Parameters & Loss (9:41)
Connecting The Dots Using The Chain Rule (5:20)
Let's Talk About Derivatives (12:49)
The Chain Rule - How Does It Work? (7:46)
Back Propagation - A Worked Example (37:18)
ANN's - Let's Summarise What We've Learned (4:40)
Good To Know: Global vs. Local Minima (4:04)
Good To Know: Optimizers (7:02)
Introduction To Keras (11:31)
Getting The Data (4:49)
Installs & Versioning (6:03)
Predicting Video Game Player Success - Part 1 (PRACTICAL) (2:45)
Predicting Video Game Player Success - Part 2 (PRACTICAL) (12:30)
Predicting Video Game Player Success - Part 3 (PRACTICAL) (14:59)
Predicting Video Game Player Success - Part 4 (PRACTICAL) (17:47)
Predicting Video Game Player Success - Part 5 (PRACTICAL) (10:15)
Deep Learning - Convolutional Neural Networks
CNN - Our Task For ABC Grocery (2:26)
Introduction To Convolutional Neural Networks (5:22)
Images: What Does A Computer See? (7:11)
CNN vs. ANN (7:15)
CNN: The Basics (11:47)
Convolutional Layers (9:04)
Pooling Layers (9:47)
Dense Layers (6:00)
Fruit Classification Project - Introduction (2:45)
Fruit Classification Project - Part 1 (PRACTICAL) (13:53)
Fruit Classification Project - Part 2 (PRACTICAL) (17:13)
Fruit Classification Project - Part 3 (PRACTICAL) (16:06)
Fruit Classification Project - Part 4 (PRACTICAL) (20:15)
Fruit Classification Project - Part 5 (PRACTICAL) (22:23)
Taking On Over-Fitting Using Dropout (8:40)
Fruit Classification Project - Dropout (PRACTICAL) (15:48)
Image Augmentation (10:21)
Fruit Classification Project - Image Augmentation (PRACTICAL) (15:24)
Transfer Learning (5:33)
Fruit Classification Project - Transfer Learning (PRACTICAL) (24:22)
Fruit Classification Project - Keras Tuner Part 1 (PRACTICAL) (18:14)
Fruit Classification Project - Keras Tuner Part 2 (PRACTICAL) (13:34)
Fruit Classification Project - Keras Tuner Part 3 (PRACTICAL) (8:27)
Good To Know: Batch Normalisation (3:55)
Add the pre-built project to your portfolio!
Deep Learning - Image Search Engine
Image Search Engine - Our Task For ABC Grocery (5:23)
Image Search Engine - Part 1 (PRACTICAL) (11:19)
Image Search Engine - Part 2 (PRACTICAL) (4:53)
Image Search Engine - Part 3 (PRACTICAL) (9:07)
Image Search Engine - Part 4 (PRACTICAL) (21:10)
Add the pre-built project to your portfolio!
Deep Learning Resource
How To Read A Research Paper
Admin
Request Your ** DSI Data Science Professional Certification **
Become a DATA SCIENCE INFINITY Affiliate!
Extension: Data Storytelling (Gilbert Eijkelenboom)
Data Storytelling Mini-Course Introduction (Andrew) (1:27)
The Presentation I Messed Up (Gilbert Introduction) (6:25)
Exercise: Spot The Presentation Mistakes (1:55)
The Power Of The Story - My Cousin Who Smoked Cigarettes (2:16)
The G.A.M.E Framework (2:49)
The G.A.M.E Framework (Downloadable PDF)
The Importance Of Knowing Your Audience (2:01)
The Importance Of Knowing Your Audience (Downloadable PDF)
Getting Audience Buy-In (The IKEA Effect) (2:14)
What Makes A Good Story - Introducing The A.B.T Framework (11:08)
Create Your A.B.T Story (1:30)
Create Your A.B.T Story (PDF Workbook)
How To Visualize Your A.B.T Story (3:40)
Extension: Docker Fundamentals (Andreas Kretz)
Docker Course Introduction (Andrew) (1:05)
Docker Course Overview (Andreas) (3:17)
Docker vs Virtual Machines (6:32)
Key Docker Terminology (6:05)
Docker Desktop - Installation Instructions (4:19)
Pulling Images & Running Containers In CLI (6:44)
CLI Cheat Sheet (3:48)
Docker Compose Explained (6:44)
Building Our First Simple Docker Image (6:37)
Building An Image Requiring Dependancies (5:14)
Using The DockerHub Image Registry (4:34)
Understanding Image Layers (8:04)
Deployment Of Containers In Production (5:56)
Security Best Practices (4:19)
Managing Docker Images & Containers With Portainer (4:14)
Docker Course Conclusion (Andreas) (2:20)
Docker Course Conclusion (Andrew) (2:03)
DSI Docker Mini-Project: Code Overview (6:03)
DSI Docker Mini-Project: Setting Everything Up (6:00)
DSI Docker Mini-Project: Building Our Docker Image Locally (3:48)
DSI Docker Mini-Project: Building & Pushing Our Docker Image To DockerHub (6:00)
DATA SCIENCE INFINITY Downloadable Resource Library
SQL: Introduction To SQL
SQL: Joins (A Guide For All Brains)
SQL: Order Of Execution
SQL: What To Expect In SQL Tests
SQL: 10 Common Theory Questions & Answers
Python: List Methods
Python: Set Methods
Python: Dictionary Methods
Python: Introduction To Numpy
Statistics: Four Types Of Data
Statistics: Common Statistical Distributions
Statistics: Common Hypothesis Tests
Statistics: The Central Limit Theorem
Machine Learning: AI vs. ML vs. DL vs. DS
Machine Learning: Supervised Learning vs. Unsupervised Learning
Machine Learning: Data Preparation & Cleaning
Machine Learning: Assessing Classification Accuracy
Machine Learning: Principal Components Analysis
Machine Learning: Linear Regression
Machine Learning: K-Means
Machine Learning: Precision vs. Recall
Deep Learning: Activation Functions
Career: 5 Conquer Imposter Syndrome In Data Science
Career: 13 Questions For Successful Data Science Projects
Interviewing: Questions To Ask Your Interviewer
Other Resources
Optimise Your Data Science Job Search - My Discussion with Kate McDermott (Recruitment Manager) (70:28)
The Skills Required To Get Into Data Science - My Discussion with Kate Strachnyi (57:29)
Insider Insight From 450+ Amazon Interviews - My Discussion with Gayle Gallagher (69:14)
Machine Learning: Principal Components Analysis
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