Book Image

Hands-On Data Science and Python Machine Learning

By : Frank Kane
Book Image

Hands-On Data Science and Python Machine Learning

By: Frank Kane

Overview of this book

Join Frank Kane, who worked on Amazon and IMDb’s machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank’s successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis.

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Table of Contents (11 chapters)
Preface
Preface
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
Getting Started
Getting Started
Installing Enthought Canopy
Using and understanding IPython (Jupyter) Notebooks
Python basics - Part 1
Understanding Python code
Importing modules
Python basics - Part 2
Running Python scripts
Summary
2
Statistics and Probability Refresher, and Python Practice
Statistics and Probability Refresher, and Python Practice
Types of data
Mean, median, and mode
Using mean, median, and mode in Python
Standard deviation and variance
Probability density function and probability mass function
Types of data distributions
Percentiles and moments
Summary
3
Matplotlib and Advanced Probability Concepts
Matplotlib and Advanced Probability Concepts
A crash course in Matplotlib
Covariance and correlation
Conditional probability
Bayes' theorem
Summary
4
Predictive Models
Predictive Models
Linear regression
Polynomial regression
Multivariate regression and predicting car prices
Multi-level models
Summary
5
Machine Learning with Python
Machine Learning with Python
Machine learning and train/test
Using train/test to prevent overfitting of a polynomial regression
Bayesian methods - Concepts
Implementing a spam classifier with Naïve Bayes
K-Means clustering
Clustering people based on income and age
Measuring entropy
Decision trees - Concepts
Decision trees - Predicting hiring decisions using Python
Ensemble learning
Support vector machine overview
Using SVM to cluster people by using scikit-learn
Summary
6
Recommender Systems
Recommender Systems
What are recommender systems?
Item-based collaborative filtering
How item-based collaborative filtering works?
Finding movie similarities
Improving the results of movie similarities
Making movie recommendations to people
Improving the recommendation results
Summary
7
More Data Mining and Machine Learning Techniques
More Data Mining and Machine Learning Techniques
K-nearest neighbors - concepts
Using KNN to predict a rating for a movie
Dimensionality reduction and principal component analysis
A PCA example with the Iris dataset
Data warehousing overview
Reinforcement learning
Summary
8
Dealing with Real-World Data
Dealing with Real-World Data
Bias/variance trade-off
K-fold cross-validation to avoid overfitting
Data cleaning and normalisation
Cleaning web log data
Normalizing numerical data
Detecting outliers
Summary
9
Apache Spark - Machine Learning on Big Data
Apache Spark - Machine Learning on Big Data
Installing Spark
Spark introduction
Spark and Resilient Distributed Datasets (RDD)
Introducing MLlib
Decision Trees in Spark with MLlib
K-Means Clustering in Spark
TF-IDF
Searching wikipedia with Spark MLlib
Using the Spark 2.0 DataFrame API for MLlib
Summary
10
Testing and Experimental Design
Testing and Experimental Design
A/B testing concepts
T-test and p-value
Measuring t-statistics and p-values using Python
Determining how long to run an experiment for
A/B test gotchas
Summary

How item-based collaborative filtering works?

Alright, let's talk about how item-based collaborative filtering works. It's very similar to user-based collaborative filtering, but instead of users, we're looking at items.

So, let's go back to the example of movie recommendations. The first thing we would do is find every pair of movies that is watched by the same person. So, we go through and find every movie that was watched by identical people, and then we measure the similarity of all those people who viewed that movie to each other. So, by this means we can compute similarities between two different movies, based on the ratings of the people who watched both of those movies.

So, let's presume I have a movie pair, okay? Maybe Star Wars and The Empire Strikes Back. I find a list of everyone who watched both of those movies, then I compare their ratings...

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