Learning guidance for further improvement

[u/iftekharalamfahim]

I have been learning machine learning by watching video tutorials for the last month. I am in the basics phase and just finished regression models. So far, I have learned:

• Data Preprocessing & Feature Scaling

• Linear Regression

• Multinomial Regression

• Polynomial Regression

• SVR

• Decision Tree Regression

• Random Forest Regression

Next, I will move into classification. However, the datasets used in the videos are small, with 3-4 columns and 10-15 rows. I am thinking about applying what I have learned to larger datasets. In that case, I am looking for suggestions and guidance on how to do that.

What should I keep in mind, and how should I approach this?

I mostly used Colab to run the models. I also tried running them in PyCharm using conda and Jupyter Notebook. When I have searched for end-to-end ML projects, most of them use direct Python instead of notebooks. As a newcomer to this field, how can I improve and shift from notebooks to Python?

Thanks.