2016

9:00 hours

Section 1: Getting Started

Lecture 1

Introduction

02:44

Lecture 2

[Activity] Getting What You Need

02:37

Lecture 3

[Activity] Installing Enthought Canopy

06:19

Lecture 4

Python Basics, Part 1

15:58

Lecture 5

[Activity] Python Basics, Part 2

09:41

Lecture 6

Running Python Scripts

03:55

Section 2: Statistics and Probability Refresher, and Python Practise

Lecture 7

Types of Data

06:58

Lecture 8

Mean, Median, Mode

05:26

Lecture 9

[Activity] Using mean, median, and mode in Python

08:30

Lecture 10

[Activity] Variation and Standard Deviation

11:12

Lecture 11

Probability Density Function; Probability Mass Function

03:27

Lecture 12

Common Data Distributions

07:45

Lecture 13

[Activity] Percentiles and Moments

12:33

Lecture 14

[Activity] A Crash Course in matplotlib

13:46

Lecture 15

[Activity] Covariance and Correlation

11:31

Lecture 16

[Exercise] Conditional Probability

11:03

Lecture 17

Exercise Solution: Conditional Probability of Purchase by Age

02:18

Lecture 18

Bayes’ Theorem

05:23

Section 3: Predictive Models

Lecture 19

[Activity] Linear Regression

11:01

Lecture 20

[Activity] Polynomial Regression

08:04

Lecture 21

[Activity] Multivariate Regression, and Predicting Car Prices

08:06

Lecture 22

Multi-Level Models

04:36

Section 4: Machine Learning with Python

Lecture 23

Supervised vs. Unsupervised Learning, and Train/Test

08:57

Lecture 24

[Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression

05:47

Lecture 25

Bayesian Methods: Concepts

03:59

Lecture 26

[Activity] Implementing a Spam Classifier with Naive Bayes

08:05

Lecture 27

K-Means Clustering

07:23

Lecture 28

[Activity] Clustering people based on income and age

05:14

Lecture 29

Measuring Entropy

03:09

Lecture 30

[Activity] Install GraphViz

Article

Lecture 31

Decision Trees: Concepts

08:43

Lecture 32

[Activity] Decision Trees: Predicting Hiring Decisions

09:47

Lecture 33

Ensemble Learning

05:59

Lecture 34

Support Vector Machines (SVM) Overview

04:27

Lecture 35

[Activity] Using SVM to cluster people using scikit-learn

05:36

Section 5: Recommender Systems

Lecture 36

User-Based Collaborative Filtering

07:57

Lecture 37

Item-Based Collaborative Filtering

08:15

Lecture 38

[Activity] Finding Movie Similarities

09:08

Lecture 39

[Activity] Improving the Results of Movie Similarities

07:59

Lecture 40

[Activity] Making Movie Recommendations to People

10:22

Lecture 41

[Exercise] Improve the recommender’s results

05:29

Section 6: More Data Mining and Machine Learning Techniques

Lecture 42

K-Nearest-Neighbors: Concepts

03:44

Lecture 43

[Activity] Using KNN to predict a rating for a movie

12:29

Lecture 44

Dimensionality Reduction; Principal Component Analysis

05:44

Lecture 45

[Activity] PCA Example with the Iris data set

09:05

Lecture 46

Data Warehousing Overview: ETL and ELT

09:05

Lecture 47

Reinforcement Learning

12:44

Section 7: Dealing with Real-World Data

Lecture 48

Bias/Variance Tradeoff

06:15

Lecture 49

[Activity] K-Fold Cross-Validation to avoid overfitting

10:55

Lecture 50

Data Cleaning and Normalization

07:10

Lecture 51

[Activity] Cleaning web log data

10:56

Lecture 52

Normalizing numerical data

03:22

Lecture 53

[Activity] Detecting outliers

07:00

Section 8: Apache Spark: Machine Learning on Big Data

Lecture 54

[Activity] Installing Spark – Part 1

07:02

Lecture 55

[Activity] Installing Spark – Part 2

13:29

Lecture 56

Spark Introduction

09:10

Lecture 57

Spark and the Resilient Distributed Dataset (RDD)

11:42

Lecture 58

Introducing MLLib

05:09

Lecture 59

[Activity] Decision Trees in Spark

16:00

Lecture 60

[Activity] K-Means Clustering in Spark

11:07

Lecture 61

TF / IDF

06:44

Lecture 62

[Activity] Searching Wikipedia with Spark

08:11

Section 9: Experimental Design

Lecture 63

A/B Testing Concepts

08:23

Lecture 64

T-Tests and P-Values

05:59

Lecture 65

[Activity] Hands-on With T-Tests

06:04

Lecture 66

Determining How Long to Run an Experiment

03:24

Lecture 67

A/B Test Gotchas

09:26

Section 10: You made it!

Lecture 68

More to Explore

02:59

Lecture 69

Don’t Forget to Leave a Rating!

Article

Lecture 70

Bonus Lecture: Discounts on my Spark and MapReduce courses!

01:28