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Making Predictions with Data and Python [Video]

More Information
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
About

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.

Features
  • Understand the core concepts in Predictive Analytics and how to apply them to build predictive models in diverse fields
  • Effectively use Python's main library, scikit-learn, for Predictive Analytics and Machine Learning 
  • Learn the foundational models and algorithms that are required for any job in the field of Predictive Analytics
Course Length 4 hours 10 minutes
ISBN9781788297448
Date Of Publication 30 Aug 2017
The Anaconda Distribution
The Jupyter Notebook
NumPy – The Foundation for Scientific Computing
Using Pandas for Analyzing Data
How to Do Predictive Analytics?
Machine Learning – Supervised Versus Unsupervised Learning
Supervised Learning – Regression and Classification
Models and Algorithms

Authors

Alvaro Fuentes

Alvaro Fuentes is a data scientist with more than 12 years of experience in analytical roles. He holds an M.S. in applied mathematics and an M.S. in quantitative economics. He worked for many years 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, medicine, and mass media, among others.

He is a big Python fan and has been using it routinely for five years to analyze data, build models, produce reports, make predictions, and build interactive applications that transform data into intelligence.