Making Predictions with Data and Python [Video]

Alvaro Fuentes
Thursday, August 31, 2017

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.

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

Alvaro Fuentes

Alvaro Fuentes is a Data Scientist with an M.S. in Quantitative Economics and a M.S. in Applied Mathematics with more than 10 years of experience in analytical roles. He worked in the Central Bank of Guatemala as an Economic Analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in Data Science topics and has been a consultant for many projects in fields such as; Business, Education, Psychology and Mass Media. He also has taught many (online and in-site) courses to students from around the world in topics like Data Science, Mathematics, Statistics, R programming and Python.

Alvaro Fuentes is a big Python fan and has been working with Python for about 4 years and uses it routinely for analyzing data and producing predictions. He also has used it in a couple of software projects. He is also a big R fan, and doesn't like the controversy between what is the “best” R or Python, he uses them both. He is also very interested in the Spark approach to Big Data, and likes the way it simplifies complicated
things. He is not a software engineer or a developer but is generally interested in web technologies.

He also has technical skills in R programming, Spark, SQL (PostgreSQL), MS Excel, machine learning, statistical analysis, econometrics, mathematical modeling.

Predictive Analytics is a topic in which he has both professional and teaching experience. Having solved practical problems in his consulting practice using the Python tools for predictive analytics and the topics of predictive analytics are part of a more general course on Data Science with Python that he teaches online.