Course Intro With Data Analysis

  • Installation Setup and Overview

  • IDEs and Course Resources

  • iPython/Jupyter Notebook Overview

Learning Intro to numpy

  • Intro to numpy

  • Creating arrays

  • Using arrays and scalars

  • Indexing Arrays

  • Array Transposition

  • Universal Array Function

  • Array Processing

  • Array Input and Output

Pandas

  • Series

  • DataFrames

  • Index objects, Reindex

  • Drop Entry , Selecting Entries

  • Data Alignment

  • Rank and Sort

  • Summary Statistics

  • Missing Data

  • Index Hierarchy

    • Working with data

    • Reading and Writing Text Files

    • JSON with Python

    • HTML with Python

    • Microsoft Excel files with Python

    • Merge ,Merge on Index

    • Concatenate

    • Combining DataFrames

    • Reshaping Pivoting

    • Duplicates in DataFrames

    • Mapping Replace

    • Rename Index

    • Binning,Outliers,Permutation

    • GroupBy on DataFrames, GroupBy on Dict and Series

    • Splitting Applying and Combining

    • Cross Tabulation

Data Visualization

  • Installing Seaborn

  • Histograms

  • Kernel Density Estimate Plots

  • Combining Plot Styles

  • Box and Violin Plots ,Regression Plots

  • Heatmaps and Clustered Matrices

Introduction to Machine Learning

  • Applications of Machine Learning

  • Supervised vs Unsupervised Learning

  • Python libraries suitable for Machine Learning

Unsupervised Learning

  • K-Means Clustering

  • Hierarchical Clustering

  • Density-Based Clustering

Introduction to Machine Learning

  • Applications of Machine Learning

  • Supervised vs Unsupervised Learning

  • Python libraries suitable for Machine Learning

Introduction to Machine Learning

  • Applications of Machine Learning

  • Supervised vs Unsupervised Learning

  • Python libraries suitable for Machine Learning

Introduction to Machine Learning

  • Applications of Machine Learning

  • Supervised vs Unsupervised Learning

  • Python libraries suitable for Machine Learning

Introduction to Machine Learning

  • Applications of Machine Learning

  • Supervised vs Unsupervised Learning

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