Udemy – Data Science and Machine Learning in Python: Linear models 2022-11

Udemy – Data Science and Machine Learning in Python: Linear models 2022-11 Downloadly IRSpace

Apr 17, 2025 - 19:51 Updated: Apr 21, 2025 - 09:54

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Udemy – Data Science and Machine Learning in Python: Linear models 2022-11

Course Data Science and Machine Learning in Python: Linear models. Companies face a problem: they collect and store massive amounts of data every day. The problem is that they do not have the necessary tools and ability to extract knowledge and make decisions from this data. But this situation is changing. The demand for data scientists has increased exponentially in the last few years. The demand is so high that there are not enough people with these skills to fill all the positions. With a simple search on job sites like Indeed, you’ll see why salaries for data scientists have risen significantly in recent years.

Almost all courses on the market are either too theoretical or too practical. College courses typically don’t build the skills necessary to solve data science problems from scratch, nor do they teach you how to use the necessary software fluently. On the other hand, many online courses and bootcamps teach you how to use these techniques without having a deep understanding of them and skimming through the theory.

This course combines the best of both methods. On the one hand, we look at the origin of these methods and why they are used and understand why they work the way they do. Alternatively, we program these methods from scratch using the most popular data science and machine learning libraries in Python. Only when you understand exactly how each algorithm works will we learn how to use them with advanced Python libraries.

course content

An introduction to machine learning and data science
Simple Linear Regression: We learn how to study the relationship between different phenomena.
Multiple linear regression: We create models with more than one variable to study the behavior of a desired variable.
Lasso Regression: An advanced version of multiple linear regression with the ability to filter out the most useful variables
Ridge regression: A more robust version of multiple linear regression
Logistic regression: the most popular classification and diagnosis algorithm. This algorithm allows us to study the relationship between different variables and certain classes of objects.
Poisson Regression: An algorithm that allows us to see how several variables affect the number of times an event occurs.
Key concepts in data science (overfitting vs. underfitting, data validation, variable preparation, etc.)

What you will learn in the course:

Implementing all our models from scratch, step by step. You will learn all their theoretical and practical details.
A fundamental understanding of the most popular machine learning algorithms
Mastery of the main machine learning libraries in Python: scikit-learn, numpy, pandas, matplotlib, etc.
Understanding the data science workflow and how to solve a forecasting problem from start to finish
Diagnosing and solving problems in our models. You will be the person your colleagues come to when their models fail.

Who is this course suitable for?

Students who are interested in finding a job in data science / machine learning.
Professionals who want to apply predictive modeling to solve their toughest business problems.
Machine learning practitioners who want a deep understanding of how their algorithms work.