Advanced Course in python for Data Science:

Numpy library

?        1D Numpy array

?        2D Numpy array

?        Matrix Manipulation

?        Basic statistics with Numpy

?        Linear Transformation in Matrix

?        Transpose of a matrix

?        Inverse of a Matrix

?        Matrix Multiplication

Data Frames and Feature Vectors (Introduction to pandas)

?        Feature Premiers

?        Determining the Features

?        Manipulating Data

?        Feature Representation

?        Data Munging

?        Feature Extraction

Data Exploration (Matplotlib/Seaborn/ggplot/Altair/Plotly (for Visualization)

?        Scatter Plots using Seaborn/Altair

?        Bar Charts

?        Line charts

?        Faceting

?        Histograms

?        Box Plots

?        Visualizing Multidimensional data

?        Colour Maps

?        Aesthetics

?        Geo-Mapping

Data Wrangling

?        Transformation

?        Accessing Data Through APIs

?        Data in JSON format

?        Handling Missing Values

?        Data Cleansing

Predictive Modeling (Introduction to scikit-learn ( for machine learning Library))

?        Splitting Data

?        Clustering       

?        K-Nearest Neighbors

?        Regression

Advanced Modelling

?        SVM

?        Decision Trees

?        Random Forest

Model Evaluation

?        Confusion

?        Cross Validation

?        Parameter Tuning

Introduction to Natural Language Processing (nltk/spacy )

?        Read Textual Data

?        Count Vectorizer

?        Stop words

?        Lemmatization

?        Stemming

?        Bag of Words

?        Naive Bayes Model


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