Data Science and Machine Learning with Python Masterclass

Learn To Data Science and Machine Learning with Python

About This Class
Learn To Data Science and Machine Learning with Python

In this practical, hands-on class you're going to learn how to use Python for Data Science and Machine Learning!

This course includes two data science and machine learning projects that you can follow step by step!

Even if you already have some experience, or want to learn about the advanced features of Machine Learning and Data Science with Python, this course is for you!

In this class you’ll learn:

Data cleaning, processing, wrangling, visualization, and manipulation
Machine Learning and its various practical applications
How to use the various scripting and libraries within Python
Machine Learning concepts and algorithms
Supervised vs unsupervised Machine Learning
Regression, classification, clustering, and sci-kit learn
How to build custom data solutions
How to create a professional data scientist resume
No matter what the scenario or how complicated a data problem may be, this class gives you the foundational training you need to solve real-world problems using Data Science and Machine Learning with Python – and start pursuing a career in a field that is increasingly in demand as the global reliance on technology grows.

Project Description
For this class project, follow the step-by-step lessons in the course and use the "decision tree" lessons to build a model that predicts whether income exceeds $50K/year using the dataset.

Use the "decision tree" lessons as a reference. Follow these steps:

Load the dataset and perform the proper data preprocessing/cleaning. Justify all your steps. Elaborate and describe as much as possible.
Run our implementation on the complete dataset. Compare the accuracy you get with the one we got before. Explain why the results are either better or worse.
Perform hyperparameter tuning using grid-search.
Visualize the tree by using sklearn functions. Discuss the first two features and attributes that the tree was split on. Do they make sense to you? Why?
Plot the feature importance, and choose the most important 5 of them and run our algorithm only on them. Compare the accuracy you get with the accuracy you got on the full dataset.
Once you've completed the project, submit it to the class.