Data Science &

Machine Learning With Python

CURRICULUM


AG01.    Install Python and Jupyter environment, a powerful framework for data analysis


AG02.    Working with Python,  iPython


AG03.    Use the Jupyter notebook environment


AG04.    Programming concepts in Python


AG05.    Different Data structures in Python


AG06.    How to work with various data formats within python, including JSON, and                         MS-Excel Worksheets


AG07.    Learn Numpy - a common scientific computation library


AG08.    Getting familiar with Pandas for structural data processing


AG09.    Building informative, useful and beautiful visualizations dashboards using Matplotlib and Seaborn libraries in Python


AG10.    Getting familiar with clustering in data mining


AG11.    Python data science handbook essential tools for working with data


AG12.    Learn the common statistical data analysis techniques in python


AG13.    Predicting Data with time or time series forecasting


AG14.    Basic Statistics for data science course covering standard deviation, covariance,    correlation, autocorrelation


AG15.    Life-cycle of a machine learning project


AG16.    Life of a Data analyst, Data scientist


AG17.    Step-by-step execution for creating a Machine learning model


AG18.    Introduction to machine learning


AG19.    Classification and Overview of Different techniques in Machine learning


AG20.    Use scikit-Learn(sklearn) for machine learning tasks


AG21.    Understanding types of data and their uses


AG22.    Getting familiar with simple linear regression


AG23.    Generalizing prediction of the continuous variable with Multiple Linear Regression


AG24.    Cross-verification method for creating better models


AG25.    Model Evaluation using multiple techniques like RMSE

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