Python for Data Analytics and Machine Learning Bootcamp
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Training TypeLive Training
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CategoryPython
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Duration25 Hours
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Rating4.9/5
Python for Data Analytics Course Introduction
About Python for Data Analytics Course
Python for Data Analytics and Machine Learning Bootcamp enables you to take your data science skills into a variety of companies, helping them analyze data and make more informed business decisions. The course covers basic and advanced Python, data exploration, data visualization, descriptive analytics, and predictive analytics techniques with the Python language. You will learn about various Python packages like Numpy, Pandas, Scikit Learn, Seaborn. You will also get a chance to learn how to import and export data in using Python, data structures in Python, various statistical concepts, cluster analysis, Regression and classification.
Python for Data Analytics Course Objective
Install Python, Jupyter Notebook and learn about the various Python packages
Gain an in-depth understanding of data structure used in Python and learn to import/export data in Python
Define, understand and use the various functions in Python
Learn to do data visualization using Python packages like matplotlib and seaborn
Gain a basic understanding of various statistical concepts
Understand and use linear regression model, and classification techniques in Machine Learning
Learn and use clustering methods including k-means and hierarchical clustering
Who is the Python for Data Analytics Course Target Audience?
This course is meant for all those students and professionals who are interested in using the Python's powerful ecosystem
What Basic Knowledge Required to Learn Python for Data Analytics?
There are no prerequisites
Available Batches
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Various datatypes in Python
Lists
Tuples
Dictionaries
Sets
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While Loops
For Loops
If Else statements
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How to create the functions
Benefits of using the functions
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Introduction to String operations and RE Module
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Understand the exception Handling using Python
Understand the TRY-EXCEPT-ELSE-FINALLY block
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Missing Data
Categorical Data
Feature Scaling
Data Split (Test and Training Set)
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Simple Linear Regression
Multiple Linear Regression
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Logistic Regression
K Nearest Neighbours (K-NN)
Support Vector Machine (SVM)
Navie Bayes
Decision Tree Classification
Random Forest Classification
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K Means
Hierarchical Clustering
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Apriori
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K Fold Technique
Grid Search