MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

June 28, 2019 | ISBN: 9781838986063 | English

Duration: 51 Lessons (6h 7m) | Size: 3.67 GB

Use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs and extract meaningful insights.

Learn

Install the required packages to set up a data science coding environment

Load data into a Jupyter Notebook running Python

Use Matplotlib to create data visualizations

Fit a model using scikit-learn

Use lasso and ridge regression to reduce overfitting

Fit and tune a random forest model and compare performance with logistic regression

Create visuals using the output of the Jupyter Notebook

About

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions.

Features

Practical guidance on industry-standard data analysis and machine learning tools

Tune algorithms to provide the best predictions on new unseen data

Gain knowledge on how to prepare data and feed it to machine learning algorithms

Gain insight into the predictive capabilities of the models and the way they make predictions