Python Statistical Methods: Machine Learning & Data Science

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This course is ideal for you if you want to gain knowledge in statistical methods required for Data Science and machine learning!

Learning Statistics is an essential part of becoming a professional data scientist. Most data science learners study python for data science and ignore or postpone studying statistics. One reason for that is the lack of resources and courses that teach statistics for data science and machine learning.

Statistics is a huge field of science, but the good news for data science learners is that not all statistics are required for data science and machine learning. However, this fact makes it more difficult for learners to study statistics because they are not sure where to start and what are the most relevant topics of statistics for data science.

This course comes to close this gap.

This course is designed for both beginners with no background in statistics for data science or for those looking to extend their knowledge in the field of statistics for data science.

I have organized this course to be used as a video library for you so that you can use it in the future as a reference. Every lecture in this comprehensive course covers a single topic.

In this comprehensive course, I will guide you to learn the most common and essential methods of statistics for data analysis and data modeling.

My course is equivalent to a college-level course in statistics for data science and machine learning that usually cost thousands of dollars. Here, I give you the opportunity to learn all that information at a fraction of the cost! With 77 HD video lectures, many exercises, and two projects with solutions.

All materials presented in this course are provided in detailed downloadable notebooks for every lecture.

Most students focus on learning python codes for data science, however, this is not enough to be a proficient data scientist. You also need to understand the statistical foundation of python methods. Models and data analysis can be easily created in python, but to be able to choose the correct method or select the best model you need to understand the statistical methods that are used in these models. Here are a few of the topics that you will be learning in this comprehensive course:

· Data Types and Structures

· Exploratory Data Analysis

· Central Tendency Measures

· Dispersion Measures

· Visualizing Data Distributions

· Correlation, Scatterplots, and Heat Maps

· Data Distribution and Data Sampling

· Data Scaling and Transformation

· Data Scaling and Transformation

· Confidence Intervals

· Evaluation Metrics for Machine Learning

· Model Validation Techniques in Machine Learning

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