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  • 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 machine learning models using scikit-learn
  • Use lasso and ridge regression to regularize your models
  • Compare performance between models to find the best outcomes
  • Use k-fold cross-validation to select model hyperparameters

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions.

By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.

  • Tackle data science problems by identifying the problem to be solved
  • Illustrate patterns in data using appropriate visualizations
  • Implement suitable machine learning algorithms to gain insights from data
Page Count 374
Course Length 11 hours 13 minutes
ISBN 9781838551025
Date Of Publication 30 Apr 2019


Stephen Klosterman

Stephen Klosterman is a machine learning data scientist at CVS Health. He enjoys helping to frame problems in a data science context and delivering machine learning solutions that business stakeholders understand and value. His education includes a Ph.D. in biology from Harvard University, where he was an assistant teacher of the data science course.