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Programming Languages and Platforms for AI and ML

Stanford University_080921E
[Stanford University]

- Python vs. R

R and Python both share similar features and are the most popular tools used by data scientists. Both are open-source and henceforth free yet Python is structured as a broadly useful programming language while R is created for statistical analysis.

The Python programming language was developed in the late 80s and plays a crucial role in powering the internal infrastructure of Google. Python comprises of enthusiastic developers and now it’s been used in the widely used applications of YouTube, Instagram, Quora, and Dropbox. Python is broadly utilized over the IT business and permits simple effort of collaboration inside development groups. In this way, in the event that you need an adaptable and multi-reason programming language with a supporting enormous network of engineers alongside the extendable AI bundles then Python is a top pick.

R was developed by statisticians and basically for the statisticians which any developer can predict the same by looking at its syntax. As the language contains mathematical computations involved in machine learning which is derived from statistics, R becomes the right choice who wants to gain a better understanding of the underlying details and build innovative. If your project is heavily based on statistics then R can be considered as an excellent choice for narrowing down your projects which requires one-time dive into the dataset. For instance — if you like to analyze a corpus of text by deconstructing paragraphs into words or phrases to identify their patterns then R is the best choice.


Top 25 Algorithms_010321A
[Top 25 Algorithms]

 Putting the Machine Learning Pieces Together

Reading through a data science book or taking a course, it can feel like you have the individual pieces, but don’t quite know how to put them together. Taking the next step and solving a complete machine learning problem can be daunting, but preserving and completing a first project will give you the confidence to tackle any data science problem. The following will introduce a complete machine learning solution, which contains a real-world process that allows you to understand how all the parts are put together.


  • Data cleaning and formatting
  • Exploratory data analysis
  • Feature engineering and selection
  • Compare several machine learning models on a performance metric
  • Perform hyperparameter tuning on the best model
  • Evaluate the best model on the testing set
  • Interpret the model results 
  • Draw conclusions and document work 



[More to come ...]



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