Making Predictions with Data and Python [Video]

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Making Predictions with Data and Python [Video]

Alvaro Fuentes

Build Awesome Predictive Models with Python

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Video Details

ISBN 139781788297448
Course Length4 hours and 10 minutes

Video Description

Python has become one of any data scientist's favorite tools for doing Predictive Analytics. In this hands-on course, you will learn how to build predictive models with Python.

During the course, we will talk about the most important theoretical concepts that are essential when building predictive models for real-world problems. The main tool used in this course is scikit -learn, which is recognized as a great tool: it has a great variety of models, many useful routines, and a consistent interface that makes it easy to use. All the topics are taught using practical examples and throughout the course, we build many models using real-world datasets.

By the end of this course, you will learn the various techniques in making predictions about bankruptcy and identifying spam text messages and then use our knowledge to create a credit card using a linear model for classification along with logistic regression.

Style and Approach

This course introduces the main concepts, techniques, and best practices for doing Predictive Analytics with Python. Using an example-based approach, it covers all the stages in the process of building predictive models with Python. By the end of the course you will be able to build Predictive Analytics models using real-world data.

Table of Contents

The Tools for Doing Predictive Analytics with Python
The Course Overview
The Anaconda Distribution
The Jupyter Notebook
NumPy – The Foundation for Scientific Computing
Using Pandas for Analyzing Data
Visualization Refresher
Plotting with Matplotlib
Visualizing data with Pandas
Statistical Visualization with Seaborn
Concepts in Predictive Analytics
What Is Predictive Analytics?
How to Do Predictive Analytics?
Machine Learning – Supervised Versus Unsupervised Learning
Supervised Learning – Regression and Classification
Models and Algorithms
Regression: Concepts and Models
scikit-learn
The Multiple Regression Model
K-Nearest Neighbors for Regression
Lasso Regression
Model Evaluation for Regression
Regression: Predicting Crime, Stock Prices, and Post Popularity
Predicting Diamond Prices
Predicting Crime in US Communities
Predicting Post Popularity
Classification: Concepts and Models
Logistic Regression
Classification Trees
Naive Bayes Classifiers
Model Evaluation for Classification
Classification: Predicting Bankruptcy, Credit Default, and Spam Text Messages
Predicting Credit Card Default
Predicting Bankruptcy
Building a Spam Classifier
Further Topics in Predictive Analytics

What You Will Learn

  • Understand the main concepts and principles of Predictive Analytics and how to use them when building real-world predictive models.
  • Properly use scikit-learn, the main Python library for Predictive Analytics and Machine Learning.
  • Learn the types of Predictive Analytics problem and how to apply the main models and algorithms to solve real world problems.
  • Build, evaluate, and interpret classification and regression models on real-world datasets.
  • Understand Regression and Classification
  • Refresh your visualization skills

Authors

Table of Contents

The Tools for Doing Predictive Analytics with Python
The Course Overview
The Anaconda Distribution
The Jupyter Notebook
NumPy – The Foundation for Scientific Computing
Using Pandas for Analyzing Data
Visualization Refresher
Plotting with Matplotlib
Visualizing data with Pandas
Statistical Visualization with Seaborn
Concepts in Predictive Analytics
What Is Predictive Analytics?
How to Do Predictive Analytics?
Machine Learning – Supervised Versus Unsupervised Learning
Supervised Learning – Regression and Classification
Models and Algorithms
Regression: Concepts and Models
scikit-learn
The Multiple Regression Model
K-Nearest Neighbors for Regression
Lasso Regression
Model Evaluation for Regression
Regression: Predicting Crime, Stock Prices, and Post Popularity
Predicting Diamond Prices
Predicting Crime in US Communities
Predicting Post Popularity
Classification: Concepts and Models
Logistic Regression
Classification Trees
Naive Bayes Classifiers
Model Evaluation for Classification
Classification: Predicting Bankruptcy, Credit Default, and Spam Text Messages
Predicting Credit Card Default
Predicting Bankruptcy
Building a Spam Classifier
Further Topics in Predictive Analytics

Video Details

ISBN 139781788297448
Course Length4 hours and 10 minutes
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