Enroll Course: https://www.udemy.com/course/basic-statistics-regression-for-machine-learning-in-python/
For aspiring Machine Learning enthusiasts who often find themselves importing libraries and hoping for the best, there’s a new course on Udemy that promises to pull back the curtain and reveal the magic happening behind the scenes. ‘Basic Statistics & Regression for Machine Learning in Python’ is designed for the curious learner, the one who wants to understand the ‘why’ and ‘how’ of ML algorithms, even if they don’t want to get bogged down in complex mathematics.
This course takes a refreshingly practical approach, focusing on a layman’s understanding of statistical regression, the backbone of many ML algorithms. It meticulously guides you through setting up your Python environment with Anaconda, ensuring you have the necessary tools. For those new to Python, the course includes foundational sessions covering essential programming concepts like assignments, flow control, data structures (lists, tuples, dictionaries), and functions. It also introduces key libraries like NumPy for matrix calculations and Matplotlib for data visualization.
The curriculum then dives into the core statistical concepts. You’ll learn about central tendency measures (mean, median, mode), variance, standard deviation, and percentiles, with the unique advantage of performing calculations both manually and then comparing them with Python implementations. This dual approach solidifies understanding by reinforcing theoretical knowledge with practical application.
Understanding data distributions is crucial, and this course covers Normal and Uniform distributions, visualizing them and calculating key metrics like z-scores, again with both manual and Python methods. The real power of the course, however, lies in its comprehensive coverage of regression techniques. It starts with Simple Linear Regression, guiding you through manual calculation of correlation coefficients and slope equations for prediction, before showing you how to achieve the same with Python’s NumPy library.
Moving on, the course tackles Polynomial Linear Regression, explaining how to find regression coefficients and R-squared values for curved relationships, and then applying both manual calculations and Python for prediction. The complexity increases with Multiple Regression, where you’ll learn to handle multi-variable datasets, work with CSV files and dataframes, and utilize libraries like Seaborn for enhanced visualization. The manual calculation for multiple regression, while complex, is broken down into manageable steps, ensuring you grasp the underlying mechanics.
Finally, the course addresses the critical aspect of data preparation with normalization and standardization, explaining why scaling data is vital for ML algorithm performance. You’ll learn to perform these transformations both through Python code and manual calculations. The course concludes by pointing you towards further learning resources and providing access to shared code, notepad, and Jupyter notebook files, along with a course completion certificate.
For anyone looking to build a solid foundation in the statistical underpinnings of machine learning, this Udemy course is a highly recommended starting point. It bridges the gap between theoretical knowledge and practical implementation, empowering learners to truly understand and leverage the power of ML.
Enroll Course: https://www.udemy.com/course/basic-statistics-regression-for-machine-learning-in-python/