Booking options
£37.99
£37.99
On-Demand course
5 hours 5 minutes
All levels
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-Machine-Learning-in-Python
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
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.
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.
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
https://github.com/PacktPublishing/Basic-Statistics-and-Regression-for-Machine-Learning-in-Python
Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than 8 years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.