Dec

31

2019

Regression Modeling with Statistics and Machine Learning in Python

Laser 31 Dec 2019 12:58 LEARNING » e-learning - Tutorial

Regression Modeling with Statistics and Machine Learning in Python
MP4 | Video: AVC, 1280x720 30 fps | Audio: AAC, 48 KHz, 2 Ch | Duration: 5h 47m
Genre: eLearning | Language: English | Size: 804 MB

Minimal mathematical jargon.

This hands-on, regression-analysis bootcamp will help you master practical statistical modeling and machine learning in Python
Key Features
The course focuses on teaching you the most important Python data science concepts and packages, including Pandas
Implement clustering and classification models on data
Gain a thorough grounding in data science and understand which models should be used, and when.
What You Will Learn
Harness the power of Anaconda/iPython for practical data science
Read in data into the Python environment from different sources
Implement classical statistical regression modeling techniques such as linear regression in Python
Implement machine learning-based regression modeling techniques such as Random Forests and kNN for predictive modeling
Master neural network- and deep learning-based regression
About
This course is your one-shot guide to statistical and machine learning analysis. This course will teach you regression analysis (for both statistical data analysis and machine learning) in Python-all in a practical, hands-on way.
Specifically, the course will:
Take you from a basic level of statistical knowledge to a level where you can perform some of the most common advanced regression analysis-based techniques
Equip you to use Python to perform various statistical and machine learning data analysis tasks
Introduce you in a hands-on way to some of the most important statistical and machine learning concepts, so you can apply them to practical data analysis and interpretation
You will get a strong background in some of the most important statistical and machine learning concepts and their applications in regression analysis. You will be able to decide which regression analysis techniques are best suited for answering your research questions and most applicable to your data; then you'll interpret the results.
This is a practical, hands-on course-we spend time dealing with some theoretical concepts related to both statistical and machine learning regression analysis.

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