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 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.
