data science with python in 2021

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instructables Data Science With Python in 2021 by prashast1 As you will progress in data science with python training program, you will get to know the below things Statistics for data science Basic data cleaning techniques for model building Converting your raw data into a machine consumable format Working principle of machine learning models and their applicability Understanding the parameters required for checking model accuracy Deploying the model to make it available as a service Maintaining the model over a period of time With respect to the above steps, you will also learn how to use data science speci"c libraries in Python eg. Frequently used libraries in data cleaning like NumPy, pandas, spicy, groupby, merge; data plotting libraries like matplotlib, seaborn; machine learning- based modules available inside scikit learn for building various regression and classi"cation based algorithms, libraries to check model accuracy like confusion matrix, MSE, RMSE, Natural Language-based libraries like NLTK, genism, VADER. These will help learners with applied data science with python Data Science With Python in 2021: Page 1

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IgmGuru's Data Science with Python certification course has been designed after consulting some of the best in the industry and also the faculty who are teaching at some of the best universities. The reason we have done this is that we wanted to embed the topics and techniques which are practiced and are in demand in the industry – conduct them with the help of pedagogy which is followed across universities – kind of applied data science with python. In doing so, we make our learners more industry/job-ready. IgmGuru’s Data Science with Python online training course is the gateway towards your Data Science career.

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Page 1: Data Science with Python in 2021

instructables

Data Science With Python in 2021

by prashast1

As you will progress in data science with python trainingprogram, you will get to know the below things

Statistics for data science Basic data cleaning techniquesfor model building Converting your raw data into amachine consumable format Working principle ofmachine learning models and their applicabilityUnderstanding the parameters required for checkingmodel accuracy Deploying the model to make itavailable as a service Maintaining the model over aperiod of time With respect to the above steps, you will

also learn how to use data science speci c libraries inPython eg. Frequently used libraries in data cleaning likeNumPy, pandas, spicy, groupby, merge; data plottinglibraries like matplotlib, seaborn; machine learning-based modules available inside scikit learn for buildingvarious regression and classi cation based algorithms,libraries to check model accuracy like confusion matrix,MSE, RMSE, Natural Language-based libraries like NLTK,genism, VADER. These will help learners with applieddata science with python

Data Science With Python in 2021: Page 1