In this course, you’ll learn to Practical Data Science with Python: Learn Statistics, Visualization, Machine Learning & More. Enrolling in course Complete Data Science Training with Python for Data Analysis by Minerva Singh.
Course: Complete Data Science Training with Python for Data Analysis
Author: Minerva Singh
Description: Complete Guide to Practical Data Science with Python: Learn Statistics, Visualization, Machine Learning & More
Skill Level: All Levels
Content: 12.5 hours
Includes: Lifetime access
Categories: Academics / Math & Science
There is no risk: 30-day money back guarantee!
Learn Anywhere Available: iPhone, iPad, Tablets, iOS and Android
Recognition Certificate: Course Certificate of Completion.
Take the course, Complete Data Science Training with Python for Data Analysis. Here are some of the things you’ll be able to do after taking this course:
By the end of this course, you will become proficient in the use of the most common Python data science packages including Numpy, Pandas, Scikit and Matplotlib.
By the end of the course, you will install Anaconda and work within the iPytjhon/Jupyter environment, a powerful framework for data science analysis.
Upon completion of the course, you will be able to read in data from different sources(including web page data) and clean the data.
Upon completion, you will carry out data visualization and understand which techniques to apply when.
You’ll carry out data exploratory and pre-processing tasks such as tabulation, pivoting and data summarizing in Python.
At the end of the course, you will become proficient in working with real life data collected from different sources.
You will understand the difference between machine learning and statistical data analysis.
By the end of this course, you’ll carry out the most common statistical data analysis techniques in Python including t-tests and linear regression.
By the end of this course, you will evaluate the accuracy and generality of machine learning models.
By the end of the course, you will implement different unsupervised learning techniques on real life data.
Upon completion of the course, you will implement supervised learning (both in form of classification and regression) techniques on real data.
Upon completion, you will use the powerful H2o framework for implementing deep neural networks.
By the end of this course, you’ll build basic neural networks and deep learning algorithms.
Who this course is for:
This course is intended for those who wish to learn practical data science using Python.
This course is perfect for anyone interested in learning how to implement machine learning algorithms using Python.
This course is meant for anyone looking to become proficient in exploratory data analysis, statistical modeling and visualizations using iPython.
This course is aimed at people looking to work with real life data in Python.
This course is intended for anyone with a prior knowledge of Python looking to branch out into data analysis.
This course is aimed at people looking to get started in Deep Learning using Python.
This Course is an Excellent Choice!
Enroll in This Online Course Now!
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