Books & Videos

Table of Contents

Chapter: Getting Started


02m 44s

Getting What You Need

02m 37s

Installing Enthought Canopy

06m 19s

Python Basics – Part 1

15m 58s

Python Basics – Part 2

09m 41s

Running Python Scripts

03m 55s

Chapter: Statistics and Probability Refresher, and Python Practise

Types of Data

06m 58s

Mean, Median, and Mode

05m 26s

Using Mean, Median, and Mode in Python

08m 30s

Variation and Standard Deviation

11m 12s

Probability Density Function and Probability Mass Function

03m 27s

Common Data Distributions

07m 45s

Percentiles and Moments

12m 33s

A Crash Course in matplotlib

13m 46s

Covariance and Correlation

11m 31s

Conditional Probability

11m 3s

Exercise Solution – Conditional Probability of Purchase by Age

02m 18s

Bayes' Theorem

05m 23s

Chapter: Predictive Models

Linear Regression

11m 1s

Polynomial Regression

08m 4s

Multivariate Regression and Predicting Car Prices

08m 6s

Multi-Level Models

04m 36s

Chapter: Machine Learning with Python

Supervised versus Unsupervised Learning and Train/Test

08m 57s

Using Train/Test to Prevent Overfitting of a Polynomial Regression

05m 47s

Bayesian Methods – Concepts

03m 59s

Implementing a Spam Classifier with Naive Bayes

08m 5s

K-Means Clustering

07m 23s

Clustering People Based on Income and Age

05m 14s

Measuring Entropy

03m 9s

Decision Trees – Concepts

08m 43s

Decision Trees – Predicting Hiring Decisions

09m 47s

Ensemble Learning

05m 59s

Support Vector Machines (SVM) Overview

04m 27s

Using SVM to Cluster People by using scikit-learn

05m 36s

Chapter: Recommender Systems

User-Based Collaborative Filtering

07m 57s

Item-Based Collaborative Filtering

08m 15s

Finding Movie Similarities

09m 8s

Improving the Results of Movie Similarities

07m 59s

Making Movie Recommendations to People

10m 22s

Improve the Recommender's Results

05m 29s

Chapter: More Data Mining and Machine Learning Techniques

K-Nearest Neighbors – Concepts

03m 44s

Using KNN to predict a rating for a movie

12m 29s

Dimensionality Reduction and Principal Component Analysis

05m 44s

A PCA Example with the Iris Dataset

09m 5s

Data Warehousing Overview – ETL and ELT

09m 5s

Reinforcement Learning

12m 44s

Chapter: Dealing with Real-World Data

Bias/Variance Trade-off

06m 15s

K-Fold Cross-Validation to Avoid Overfitting

10m 55s

Data Cleaning and Normalization

07m 10s

Cleaning Web Log Data

10m 56s

Normalizing Numerical Data

03m 22s

Detecting Outliers

07m 0s

Chapter: Apache Spark – Machine Learning on Big Data

Installing Spark – Part 1

07m 2s

Installing Spark – Part 2

13m 29s

Spark Introduction

09m 10s

Spark and the Resilient Distributed Dataset (RDD)

11m 42s

Introducing MLLib

05m 9s

Decision Trees in Spark

16m 0s

K-Means Clustering in Spark

11m 7s


06m 43s

Searching Wikipedia with Spark

08m 11s

Using the Spark 2.0 DataFrame API for MLLib

07m 57s

Chapter: Experimental Design

A/B Testing Concepts

08m 23s

T-Tests and P-Values

05m 59s

Hands On with T-Tests

06m 4s

Determining How Long to Run an Experiment

03m 24s

A/B Test Gotchas

09m 26s

Chapter: You Made It!

More to Explore

02m 59s