# Starting With Linear Regression in Python (Summary)

Congratulations! In this course, you’ve learned one of the fundamental statistical and machine learning techniques—linear regression. Now you’re ready to put it to use in your work on statistics, machine learning, or scientific computing. With this solid foundation, you can progress toward more complex methods.

**In this video course, you’ve learned:**

- What linear regression is
- What linear regression is used for
- How linear regression works
- How to implement linear regression in Python, step by step

For more information on concepts covered in this course, you can check out:

- Using Jupyter Notebooks.
- Python Statistics Fundamentals: How to Describe Your Data
- NumPy, SciPy, and Pandas: Correlation With Python
- Linear Algebra in Python: Matrix Inverses and Least Squares

**Congratulations, you made it to the end of the course!** What’s your #1 takeaway or favorite thing you learned? How are you going to put your newfound skills to use? Leave a comment in the *discussion* section and let us know.

**00:00**
Let’s summarize what you did and guide you on some next steps that you may be interested in taking. So in this course, you’ve learned what linear regression and polynomial regression is and how to implement linear and polynomial regression in scikit-learn.

**00:16**
Here are a couple of steps you may be interested in taking after this course. You may be interested in how to describe your data in Python. I recommend the course Python Statistics Fundamentals: How to Describe Your Data, at the following link.

**00:31**
Or you may be interested in learning how to use Numpy, SciPy, and pandas, which are other data science modules in Python to do correlation in Python. For that, I recommend the course at the following link at Real Python.

**00:47**
All right, thanks for watching and following along. I hope you enjoyed the course.

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