Major imputation techniques in Machine learning, US census data, Listwise Deletion, Impute NaN with mean, Trimmed Mean, median, mode, Drop columns if >60% of data is missing
This is a 5 Part series of most important Data Pre-Processing Techniques of Machine Learning. Part 1 – Verify data types of the variables/features.
Python basic Quiz. 5 Questions. Difficulty Level: Easy to Moderate. Concept: Conditional Statements, Operators, Boolean Values
Python basic Quiz. 5 Questions. Difficulty Level: Easy to Moderate. Concept: Operators
How to impute missing values using SimpleImputer and ColumnTransformer, Disadvantages of SimpleImputer, Advantage of ColumnTransformer
plotly.express, plotly.graph_objects, Generate choropleth maps using FIPS and State codes using COVID-19 data set
Advanced Hyperparameter Tuning of Multilayer Perceptron – MLP, Weight Initialization, Nonlinearity (Activation Function), Optimizers, Batch Normalization, Dropout, Model Ensemble
Digital Image Processing, Morphological Operations, Erosion and Dilation, Rules, Examples, Sample programs using OpenCV
Spacy Lemmatization. What is Stemming, What is Lemmatization, Lemmatization in SpaCy with code, Stemming Vs Lemmatization in NLTK.
Python Zip and Enumerate Function, Exceptions in Zip, Use of Enumerate Function, Bonus Tip – Using both zip and enumerate together.