Algorithmic Challenges

• Overfitting the training data
• The model is too complex relative to the amount and noisiness of the training data. The model performs well on training data, but it doesnot generalize well
  • Simplify the model by select one with fewer parameter, by reducing the number of features in the training data or by constraining the model
  • Gather more training data
  • Reduce noise in the training data
• Underfitting the training data
• The model is too simple relative to the underlying structure of data. The model does not perform well even on the training data
  • Select a powerful model, with more parameters
  • Feed better features into the learning algorithm (feature Engineering)
  • Reduce the constraints on the model

ML PROJECT DEVELOPMENT FLOW

• Frame the problem and look into at the big picture
• Get the data
• Explore the data to gain insights
• Prepare the data for ML algorithms
• Train/explore many different models and short-list the best ones
• Fine-tune your model
• Present your solution
• Launch, monitor, and maintain

TidyModels

• The tidymodels framework is a collection of packages for modeling and machine learning using the tidyverse principles
• Refer Here for the official page of tidy models
• rsample: Provides the basic building blocks for creating and analyzing resamples of the data set
• recipes: Methods for creating data encoding and preprocessing recipes
• parsnip: Provides a tidy, unified interface to models that can be used to try a range of models without dealing with underlying package details
• tune: Facilitates hyperparameter tuning for the tidymodels packages
• yardstick: A package to estimate how well models are working using tidy data principles

Anaconda Installation

• Lets download anaconda from Refer Here
• Once the installation is finished. Launch Anaconda Navigator
  
• Select Environments
  
• Now create a new environment