Linear Regression with Python
Curso
568 Alunos já inscritos- Grasp Linear Regression, its importance in predictive analytics, and its application in real-world tasks.
- Understand the method to find Linear Regression parameters and their significance.
- Build simple to complex Linear Regression models using NumPy and Statsmodels.
- Master evaluating model performance with metrics and interpreting Overfitting and R-squared.
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Existem 4 módulos neste curso
Linear Regression is a crucial concept in predictive analytics. It is widely used by data scientists, data analytics, and statisticians as it is easy to build and interpret but powerful enough for many tasks.Escolhido por estudantes das melhores escolas
Por que as pessoas escolhem o Codefinity para sua carreira

Kwizera Mugisha
The teaching methodology at Codefinity is excellent, and I particularly appreciate how it has prepared me to handle real-world coding problems. Currently, I am delving into Node.js and eagerly anticipate building full-stack projects that integrate all the knowledge I have gained.

Sherry Barnes-Fox
My first course was 4 hours, I did it in a few days, "nugget-style. The instructions are very clear and easy to understand. There is even a hint to help you get the answer, and if you still cannot get the answer, then you can display the answer. I love the learning style that is used, it engages me.

Bill Wagner
I have really liked the browser-based lessons that allow me to code within the lesson. The RUN button allows me to test the code I write before submitting for a grade.

Stephanie Chan
As I went through the first course of the Python track, I liked the way the course was lay out (in easy and digestible modules) with little exercises at the end of each concept.

Daniel Chinea
I have gained a lot of practical and logical thinking skills, along with patience for myself and confidence in myself that I can learn programming.

Steve Bruening
The learning was progressive and made it easy to follow along and make progress. I could feel my skills increasing and building on each other as the course went along.
Avaliações de alunos
4.6
47 avaliações
5
70%
4
17%
3
13%
2
0%
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Mostrando 3 de 47 avaliações
Kevin Harris
5
Avaliado em Apr 9, 2025
Eoghan Gormley
4
Avaliado em Mar 22, 2025
A lot of small errors, but overall good
Gabriele Peyrano
5
Avaliado em Jan 18, 2025
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