Course Structure

Get an overview of the structure and strengths of this data science course.

We'll cover the following

About this course

This course consists of 9 chapters, 98 lessons, and 7 projects (challenges). A brief explanation of each chapter is provided below:

  1. Introduction: Provides an overview of the course, including the intended audience, prerequisites, and course structure.

  2. Data Exploration and Cleaning: Gets you started with Python and Jupyter notebooks. The chapter then explores the case study dataset and delves into exploratory data analysis, quality assurance, and data cleaning using pandas.

  3. Introduction to scikit-learn and Model Evaluation: Introduces you to the evaluation metrics for binary classification models. You’ll learn how to build and evaluate binary classification models using scikit-learn.

  4. Details of Logistic Regression and Feature Exploration: Dives deep into logistic regression and feature exploration. You’ll learn how to generate correlation plots of many features and a response variable and interpret logistic regression as a linear model.

  5. The Bias-Variance Trade-Off: Explores the foundational machine learning concepts of overfitting, underfitting, and the bias-variance trade-off by examining how the logistic regression model can be extended to address the overfitting problem.

  6. Decision Trees and Random Forests: Introduces you to tree-based machine learning models. You’ll learn how to train decision trees for machine learning purposes, visualize trained decision trees, and train random forests and visualize the results.

  7. Gradient Boosting, XGBoost, and SHAP Values: Introduces you to two key concepts: gradient boosting and shapley additive explanations (SHAP). You’ll learn to train XGBoost models and understand how SHAP values can be used to provide individualized explanations for model predictions from any dataset.

  8. Test Set Analysis, Financial Insights, and Delivery to the Client: Presents several techniques for analyzing a model test set for deriving insights into likely model performance in the future. The chapter also describes key elements to consider when delivering and deploying a model, such as the format of delivery and ways to monitor the model as it is being used

  9. Appendix: This chapter contains only one lesson, which is about how to set up the local system environment for this course.

The structure of this course

Course strengths

The following benefits for the learners in this course enhance the overall strength of the course, making it an appealing choice for those who wish to improve their skills in data science and machine learning.

Topic

Description


Data Science Concepts

The data science concepts covered in this course are essential because they provide a foundational understanding of key concepts in data science and machine learning. This understanding is crucial for building effective predictive models.


Python Packages

The Python packages covered in this course are beneficial because they enable you to effectively use key Python packages such as pandas, Matplotlib, and scikit-learn for data exploration, processing, visualization, and machine learning modeling.


Data Processing

Learning data processing techniques is beneficial because it helps you to handle large and complex datasets efficiently, reduce errors in analysis, and improve the accuracy of your models.



Data Visualization

You will gain a solid understanding of data visualization techniques using Matplotlib, a powerful Python library for creating visualizations. You will learn how to create effective visualizations to explore and present data, which is crucial for making informed decisions based on data analysis.

Machine Learning Models

By mastering this, you'll be able to build predictive machine learning models with scikit-learn and XGBoost.

Regression Techniques

You can reduce model overfitting by acquiring knowledge of lasso and ridge regression.


Course Projects

This course provides you with the opportunity to work on an end-to-end project based on a realistic dataset and engage in practical exercises.



Updated Content

The benefit of updated content is that you can gain knowledge and understanding of the latest advancements and techniques in the field of data science and machine learning.