AI News, Resources
As more and more new, quality materials are published for better understanding of big data and machine learning concepts, finding such latest materials is becoming increasingly challenging.
Fortunately, that really isn’t necessary if you have an indexed, properly curated and constantly updated source that gets constant feedback from its readers.
In this regularly updated post, I’ll give you resources you can use to learn about Big Data and machine learning and stay on top of the job market, including some free tools that’ll be useful for getting the job done.
weekly updates about popular articles on these topics by email Doing Data Science at Twitter It talks about how machine learning has played an increasingly prominent role across many core Twitter products that were previously not ML driven and how the data science landscape in Twitter has changed in the recent past Data Science Salary Survey 2015 the 2015 version of the Data Science Salary Survey explores patterns in tools, tasks, and compensation through the lens of clustering and linear models.
The research is based on data collected through an online 32-question survey, including demographic information Some Real World Machine Learning Examples The post talks about what are some real-world examples of applications of machine learning in the field- ranging from Computational Biology &
two-hour introduction to data analysis in R If you’re looking for a non-diamonds or non-nycflights13 introduction to R / ggplot2 / dplyr feel free to use materials from this workshop.
In this Intro to Python class, you will learn about powerful ways to store and manipulate data as well as cool data science tools to start your own analyses.
You can find many additional references here (Python, Excel, Spark, R, Deep Learning, AI, SQL, NoSQL, Graph Databses, Visualization, etc.) Top 10 R Packages to be a Kaggle Champion Across all major surveys, R has clearly dominated as one of the top programming choices for data scientists.
Here’s a list of 10 R packages that played a key role in getting a top 10 ranking in more than 15 Kaggle competitions Integrating Python and R into a Data Analysis Pipeline The first in a series of blog posts that: outline the basic strategy for integrating Python and R, run through the different steps involved in this process;
The first is a grouping of algorithms by the learning style.The second is a grouping of algorithms by similarity in form or function (like grouping similar animals together). Arriving
Machine Learning A-Z™: Hands-On Python R In Data Science
This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.
With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
Comparison of top data science libraries for Python, R and Scala [Infographic]
Each of these languages is suitable for a specific type of tasks, besides each developer chooses the most convenient tool for himself.
Primarily designed for statistical computing, R offers an excellent set of high-quality packages for statistical data collection and visualization.
Keep in mind, that the choice of programming language and the libraries that you will use, depends on specific tasks, so it’s beneficial to know what are the strong and weak sides of each of them.
Indeed, this list is not complete, many other valuable tools can and have to be examined, but it will definitely be a good starting point for your journey into data science industry.
- On Monday, September 16, 2019
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Interpretable Machine Learning Using LIME Framework - Kasia Kulma (PhD), Data Scientist, Aviva
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Joel Grus: Learning Data Science Using Functional Python
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