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Advanced Predictive Techniques with Scikit-Learn and TensorFlow

Learn advanced predictive techniques with Scikit-Learn and TensorFlow. Better the performance predictive models, build more complex models, and improve quality of your predictive models.

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

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Description

Improve the performance predictive models, build more complex models and use techniques to improve quality of your predictive models.

Ensemble methods offer a powerful way to improve prediction accuracy by combining in a clever way predictions from many individual predictors. In this course, you will learn how to use ensemble methods to improve accuracy in classification and regression problems.

When using Predictive Analytics to solve actual problems, besides models and algorithms there are many other practical considerations that must be considered like which features should I use, how many features are enough, should I create new features, how to combine features to give the same underlying information, which hyper-parameters should I use? We explore topics that will help you answer such questions.

Artificial Neural Networks are models loosely based on how neural networks work in a living being. These models have a long history in the Artificial Intelligence community with ups and downs in popularity. Nowadays, because of the increase in computational power, improved methods, and software enhancements, they are popular again and are the basis for advanced approaches such as Deep Learning. This course introduces the use of Deep Learning models for Predictive Analytics using the powerful TensorFlow library.

About the Author

  • Alvaro Fuentes is a Data Scientist with an M.S. in Quantitative Economics and 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 is a big Python fan, has been working with Python for about 4 years, and uses it routinely for analyzing data and producing predictions. 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 issues. He also has technical skills in R programming, Spark, SQL (PostgreSQL), machine learning, statistical analysis, econometrics, and mathematical modeling.
  • Predictive Analytics is a topic in which he has both professional and teaching experience. Having solved many practical problems in his consulting practice using Python's tools for predictive analytics he shares his experience in on these subjects in many Data Science courses he teaches online.

Basic knowledge
  • Knowledge of Python and familiarity with its Data Science Stack are assumed. Additionally, an understanding of the basic concepts of predictive analytics and how to use basic predictive models is also necessary to take full advantage of this course

What will you learn
  • Use ensemble algorithms to combine many individual predictors to produce better predictions
  • Apply advanced techniques such as dimensionality reduction to combine features and build better models
  • Evaluate models and choose the optimal hyper-parameters using cross-validation
  • Learn the foundations for working and building models using Neural Networks
  • Learn different techniques to solve problems that arise when doing Predictive Analytics in the real world
Course Curriculum
Number of Lectures: 18 Total Duration: 03:44:39
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