We can’t believe you’ve found our blog. We’re so glad you’re here!
You may be wondering why we call it a blog. Well, “blog” means “web log,” and this is a place where we talk about how to use Python’s machine learning libraries to build your own hacking tools. We’re going to be talking mostly about the scikit-learn and numpy/pandas packages, because that is what we know best.
Tool topics include:
data preprocessing (mostly for text)
classification (mostly for text)
other topics that are beyond the scope of this document but could still be useful for data scientists.
Learn how to hack with machine learning in this Machine Learning For Hackers Pdf and gain a better understanding of how hacking work.
About The Book Machine Learning For Hackers Pdf Free Download
If you’re an experienced programmer interested in crunching data, this Machine Learning For Hackers Python Pdf will get you started with machine learning a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.
Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning For Hackers Python Pdf is ideal for programmers from any background, including business, government, and academic research.
- Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text
- Use linear regression to predict the number of page views for the top 1,000 websites
- Learn optimization techniques by attempting to break a simple letter cipher
- Compare and contrast U.S. Senators statistically, based on their voting records
- Build a “whom to follow” recommendation system from Twitter data
Table Of Contents
Chapter 1 Using R
R for Machine Learning
Chapter 2 Data Exploration
Exploration versus Confirmation
What Is Data?
Inferring the Types of Columns in Your Data
Means, Medians, and Modes
Standard Deviations and Variances
Exploratory Data Visualization
Visualizing the Relationships Between Columns
Chapter 3 Classification: Spam Filtering
This or That: Binary Classification
Moving Gently into Conditional Probability
Writing Our First Bayesian Spam Classifier
Chapter 4 Ranking: Priority Inbox
How Do You Sort Something When You Don’t Know the Order?
Ordering Email Messages by Priority
Writing a Priority Inbox
Chapter 5 Regression: Predicting Page Views
Predicting Web Traffic
Chapter 6 Regularization: Text Regression
Nonlinear Relationships Between Columns: Beyond Straight Lines
Methods for Preventing Overfitting
Chapter 7 Optimization: Breaking Codes
Introduction to Optimization
Code Breaking as Optimization
Chapter 8 PCA: Building a Market Index
Chapter 9 MDS: Visually Exploring US Senator Similarity
Clustering Based on Similarity
How Do US Senators Cluster?
Chapter 10 kNN: Recommendation Systems
The k-Nearest Neighbors Algorithm
R Package Installation Data
Chapter 11 Analyzing Social Graphs
Social Network Analysis
Hacking Twitter Social Graph Data
Analyzing Twitter Networks
Chapter 12 Model Comparison
SVMs: The Support Vector Machine
Works Cited books and publications bibliography of resources books and publications; website resources statistics resources for machine learning resources for programming language resources for Colophon.
About The Author Machine Learning For Hackers Python Pdf Free Download
Drew Conway is a PhD candidate in Politics at NYU. He studies international relations, conflict, and terrorism using the tools of mathematics, statistics, and computer science in an attempt to gain a deeper understanding of these phenomena. His academic curiosity is informed by his years as an analyst in the U.S. intelligence and defense communities.
John Myles White is a PhD candidate in Psychology at Princeton. He studies pattern recognition, decision-making, and economic behavior using behavioral methods and fMRI. He is particularly interested in anomalies of value assessment.