English | Size: 804.01 MB
Genre: eLearning
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.
All the codes and supporting files for this course are available at –
https://github.com/PacktPublishing/Regression-Modeling-With-Statistics-and-Machine-Learning-in-Python
Features
Minimal mathematical jargon. 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.
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