What you would learn in Data Science and Machine Learning in Python: Linear models course?
Nearly every course available is either too academic or not practical enough. The courses offered by universities don't typically provide the necessary skills to tackle the data science challenges starting from scratch or teach students how to use the required software proficiently. However, numerous boot camps and online courses provide you with the knowledge to apply these techniques, but without having an in-depth understanding of them and only going over the basics only superficially.
The course we will teach you will combine the best aspects of each. On the one hand, we'll examine where these techniques originate from and how they are employed, and the reasons behind why they function as they do. On the other hand, we will code these algorithms by hand, employing the most widely used machine learning and data science libraries available in Python. Once you've mastered the specifics of each algorithm, then we will be able to apply them using advanced Python libraries.
A brief introduction to data science and machine learning.
Simple linear regression. We will be taught how to understand the connection between various phenomenon.
Multiple linear regression. We will build models using multiple variables to analyze the behavior of the particular variable.
Lasso regression. A more advanced version of multi-linear regression, with the capability to select the most relevant variables.
Ridge regression. A more stable variant of multi-linear regression.
Logistic regression. The most well-known algorithms for classification and detection. It allows us to examine the relationship between various variables and specific object classes.
Poisson regression. The algorithm allows us to understand how a variety of factors affect the frequency at which an event takes place.
The central concepts of data science (overfitting, overfitting vs. underfitting, cross-validation, variable prep, etc.).
Content of the Course:
Apply all of our methods starting from scratch, step by step. You will master every aspect of their theory and how to apply them.
Comprehend the most widely used algorithmic machine-learning algorithms.
Learn to master the critical machine learning libraries available in Python Numpy, scikit-learn pandas, matplotlib, etc.
Learn about the workflow of data science and the best way to tackle any prediction issue from beginning to end.
Find and fix problems within our models. You'll be the one whom your colleagues turn to for help when their models fail.
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