About this Module

This lesson discusses the intended audience and the necessary prerequisites of the module.

We'll cover the following

Who is this module for?

This module is designed for anyone who wants to become familiar with the basics of machine learning with data analysis and algorithm selection through job-focused lessons and hands-on practice.

Module structure

This module contains eight main chapters, namely:

  1. Data Manipulation with NumPy teaches us the basics of NumPy and how it’s used for data manipulation.
  2. Data Analysis with pandas teaches us the basics of pandas and its use in data analysis.
  3. Data Preprocessing with scikit-learn teaches us the techniques of data preprocessing using scikit-learn.
  4. Data Modeling with scikit-learn teaches us the regressions and decision trees data modeling techniques using scikit-learn.
  5. Clustering with scikit-learn teaches us to use clustering algorithms in scikit-learn.
  6. Gradient Boosting with XGBoost teaches us the basics of XGBoost and their application in gradient boosting.
  7. Deep Learning with TensorFlow teaches us an end-to-end deep-learning model application using TensorFlow.
  8. Deep Learning with Keras teaches us to use an end-to-end deep-learning model application using Keras.