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Making Predictions with Data and Python

How do you predict data in Python? This course will give you an understanding of the most important theoretical concepts that are essential when building predictive models for real-world problems.

Features Includes:
  • Self-paced with Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App

Course Preview Video

Description

Build Awesome Predictive Models with Python

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.

About the Author

  • 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' 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 on-site) courses to students from around the world in topics such as Data Science, Mathematics, Statistics, R programming, and Python. Alvaro Fuentes is a big Python fan, has been working with it for about 4 years, and uses it routinely for analyzing data and producing predictions. He has also used it in a couple of software projects. He is also a big R fan, and doesn't like the controversy inherent in any attempt to evaluate which 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 neither a software engineer nor 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, and mathematical modeling. Predictive Analytics is a topic in which he has both professional and teaching experience. He has solved practical problems in his consulting practice using the Python tools for predictive analytics; the topics of predictive analytics are part of a more general course on Data Science with Python that he teaches online.

Basic knowledge
  • Knowledge of the Python programming language is assumed.
  • Basic familiarity with Python's Data Science Stack would be useful, although a brief review is given.
  • Familiarity with basic mathematics and statistical concepts is also advantageous to take full advantage of this course.

What will you 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
Course Curriculum
Number of Lectures: 29 Total Duration: 04:10:50
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