Basics of Machine Learning

• Basics of machine learning, understading the decision tree, tree defth, leaf nodes
• Loading csv data using Pandas, understanding data using describe and head methods of pandas
• Building first Model, Chooing Feaures (know data used for prediction) and Target (the value to predict) Defining a model, Fitting a model, Predict, Evaulate (using sklearn lib. of python)
• Validating the models, introdution to Mean Absolute Error (MEA- diff b/w the acutal and predicted values from the training data set), introduction to train_test_split - splitting the given data into training data and data set for prediction so that we can compare the results
• Underfitting and overfitting concepts based on Shallow and Deep trees respectively. In underfitting we ignore a lot of features while in overfitting the issue is we have large splits that results in less number of records to predict [Lesser the tree depth the more we go to underfitting and more the depth we move towards overfitting. to overcome this issue we need to find a middle way!]
• Introduction to Random Forest (RandomForestRegressor) to overcomes the problem of underfitting and overfitting resulting in better model selection. Provided by sklearn lib. itself

While Choosing a better model can be quite a task, There are Google and AWS services that offer AutoML that decide the better models automatically and we just use them for predection pretty straight forward.

Stay Tuned to Lean more concepts quickly!