Learn Python
Learn Data Structure & Algorithm
Learn Numpy
Learn Pandas
Learn Matplotlib
Learn Seaborn
Learn Statistics
Learn Math
Learn MATLAB
Learn Machine learning
Learn Github
Learn OpenCV
Introduction
Setup
ANN
Working process ANN
Propagation
Bias parameter
Activation function
Loss function
Overfitting and Underfitting
Optimization function
Chain rule
Minima
Gradient problem
Weight initialization
Dropout
ANN Regression Exercise
ANN Classification Exercise
Hyper parameter tuning
CNN
CNN basics
Convolution
Padding
Pooling
Data argumentation
Flattening
Create Custom Dataset
Binary Classification Exercise
Multiclass Classification Exercise
Transfer learning
Transfer model Basic template
RNN
How RNN works
LSTM
Bidirectional RNN
Sequence to sequence
Attention model
Transformer model
Bag of words
Tokenization & Stop words
Stemming & Lemmatization
TF-IDF
N-Gram
Word embedding
Normalization
Pos tagging
Parser
semantic analysis
Regular expression
Learn MySQL
Learn MongoDB
Learn Web scraping
Learn Excel
Learn Power BI
Learn Tableau
Learn Docker
Learn Hadoop
Suppose we have four words: Loved, Lovely, Loves, and Love. For these four words the root word is love. It
means that all these words come from love. Now if we want to normalize these four words into root from then we
will have only one-word love. So it means we will remove four words and we will have only one word.
These types of words like loved, lovely, loves and love are called inflected words because these words are
inflected from the word love.
Suppose we have a document of 10000 words. So there will be a lot of inflected words. Now if we normalize
words means remove inflected words and only have the root word then we will be able to minimize our vector in
a very less number of columns.