封面
版权页
Credits
About the Author
About the Reviewer
www.PacktPub.com
Why subscribe?
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Chapter 1. The Groundwork – Julia's Environment
Julia is different
Setting up the environment
Using REPL
Using Jupyter Notebook
Package management
Parallel computation using Julia
Julia's key feature – multiple dispatch
Facilitating language interoperability
Summary
References
Chapter 2. Data Munging
What is data munging?
What is a DataFrame?
Summary
References
Chapter 3. Data Exploration
Sampling
Inferring column types
Basic statistical summaries
Scalar statistics
Measures of variation
Scatter matrix and covariance
Computing deviations
Rankings
Counting functions
Histograms
Correlation analysis
Summary
References
Chapter 4. Deep Dive into Inferential Statistics
Installation
Understanding the sampling distribution
Understanding the normal distribution
Type hierarchy in Distributions.jl
Univariate distributions
Truncated distributions
Understanding multivariate distributions
Understanding matrixvariate distributions
Distribution fitting
Confidence interval
Understanding z-score
Understanding the significance of the P-value
Summary
References
Chapter 5. Making Sense of Data Using Visualization
Difference between using and importall
Pyplot for Julia
Unicode plots
Visualizing using Vega
Data visualization using Gadfly
Summary
References
Chapter 6. Supervised Machine Learning
What is machine learning?
Machine learning – the process
Understanding decision trees
Supervised learning using Naïve Bayes
Summary
References
Chapter 7. Unsupervised Machine Learning
Understanding clustering
K-means clustering
Summary
References
Chapter 8. Creating Ensemble Models
What is ensemble learning?
Random forests
Implementation in Julia
Why is ensemble learning superior?
Summary
References
Chapter 9. Time Series
What is forecasting?
What is TimeSeries?
Implementation in Julia
Summary
References
Chapter 10. Collaborative Filtering and Recommendation System
What is a recommendation system?
Association rule mining
Content-based filtering
Collaborative filtering
Building a movie recommender system
Summary
Chapter 11. Introduction to Deep Learning
Revisiting linear algebra
Probability and information theory
Differences between machine learning and deep learning
Implementation in Julia
Summary
References
更新时间：2021-07-08 11:49:58
完结共106章
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