Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Hands-On Data Science with Anaconda
  • Toc
  • feedback
Hands-On Data Science with Anaconda

Hands-On Data Science with Anaconda

By : Yuxing Yan, Yan
2.6 (5)
close
Hands-On Data Science with Anaconda

Hands-On Data Science with Anaconda

2.6 (5)
By: Yuxing Yan, Yan

Overview of this book

Anaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world. The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You’ll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You’ll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod. Once you’re accustomed to all this, you’ll start with operations in data science such as cleaning, sorting, and data classification. You’ll move on to learning how to perform tasks such as clustering, regression, prediction, and building machine learning models and optimizing them. In addition to this, you’ll learn how to visualize data using the packages available for Julia, Python, and R.
Table of Contents (15 chapters)
close

Review questions and exercises

  1. What does unsupervised learning mean?
  2. What is the major difference between unsupervised learning and supervised learning?
  3. How do we install the Python package sklearn?
  4. Discuss the relationship between distance and clustering classification.
  5. How do we define the distance between two objects?
  6. For non-numeric values, how do we define a distance between two members?
  7. For R, we could find a set of related packages related to unsupervised learning called cluster. Is there any task view, or similar super package, for Python?
  8. First, generate the following set of random numbers:
>set.seed(12345) 
>n=30 
>nGroup=4 
>x <- matrix(rnorm(n*nGroup),nrow =nGroup) 

Then, based on the various definitions of distance, estimate the distances between those four groups.

  1. For the following set of data, estimate the minimum, maximum, and average distances...
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete