Basic Statistics and Regression for Machine Learning in Python

This course is for ML enthusiasts who want to understand basic statistics and regression for machine learning. The course starts with setting up the environment and understanding the basics of Python language and different libraries. Next, you’ll see the basics of machine learning and different types of data. After that, you’ll learn a statistics technique called Central Tendency Analysis.

Post this, you’ll focus on statistical techniques such as variance and standard deviation. Several techniques and mathematical concepts such as percentile, normal distribution, uniform distribution, finding z-score, linear regression, polynomial linear regression, and multiple regression with the help of manual calculation and Python functions are introduced as the course progresses.

The dataset will get more complex as you proceed ahead; you’ll use a CSV file to save the dataset. You’ll see the traditional and complex method of finding the coefficient of regression and then explore ways to solve it easily with some Python functions.

Finally, you’ll learn a technique called data normalization or standardization, which will improve the performance of the algorithms very much compared to a non-scaled dataset.

By the end of this course, you’ll gain a solid foundation in machine learning and statistical regression using Python.

All the code files and related files are available on the GitHub repository at https://github.com/PacktPublishing/Basic-Statistics-and-Regression-for-…

Type
video
Category
Programming
publication date
2021-10-26
what you will learn

Set up the environment
Learn central tendency analysis
Learn statistical models and analysis
Learn regression models and analysis
Use NumPy, matplotlib, and scikit-learn libraries
Learn the data normalization or standardization technique

duration
305
key features
A comprehensive course that includes Python coding, visualization, loops, variables, and functions * Manual calculation and then using Python functions/codes to understand the difference * Beginner to advanced mathematics and statistical concepts that cover machine learning algorithms
approach
This is a comprehensive and hands-on course to learn from basic to advanced mathematics and statistical concepts that cover machine learning algorithms. The instructor will take you through every step of the code.

The instructor shows both the manual calculation approach and then the Python functions to work around in solving statistical and regression problems.
audience
This course is for beginners and individuals who want to learn mathematics for machine learning. You need not have any prior experience or knowledge in coding; just be ready with your learning mindset at the highest level.

Individuals interested in learning what’s actually happening behind the scenes of Python functions and algorithms (at least in a shallow layman’s way) will be highly benefitted.

Basic computer knowledge and an interest to learn mathematics for machine learning is the only prerequisite for this course.
meta description
Get ready to learn the basics of machine learning and the mathematics of statistical regression, which powers almost all machine learning algorithms.
short description
This course is a perfect supplement for ML enthusiasts. If you are only just beginning your adventures in machine learning and want to know the basics of statistics and regression used for machine learning, then go for it. Discover how you can level up and gain confidence to implement statistical methods and regression in machine learning with Python.
subtitle
A quick and easy guide on statistical regression for machine learning
keywords
Statistics, Regression for Machine Learning, Python, mathematics, calculation, NumPy, scikit-learn, mean, median, mode, data visualization, normal distribution, variance, standard deviation
Product ISBN
9781803238487