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Python for Machine Learning and Data Mining

 

Python for Machine Learning and Data Mining 

GETTING STARTED

Python for Machine Learning and Data Mining Data mining is the process of discovering predictive information from the analysis of large databases. For a data scientist, data mining can be a vague and daunting task – it requires a diverse set of skills and knowledge of many data mining techniques to take raw data and successfully get insights from it.  You’ll want to understand the foundations of statistics and different programming languages that can help you with data mining at scale

What you'll learn

  • Main concepts of machine learning and data mining
  • Programming on Python language using the main scientific packages like Scikit-learn, Pandas, Numpy, etc
  • Manage real data and develop a desktop applications for machine learning and data mining

Requirements

  • Basic programming skills, lineal algebra and calculus concepts.

Description

Data Mining and Machine Learching are a hot topics on business intelligence strategy on many companies in the world. These fields give to data scientists the opportunity to explore on a deep way the data, finding new valuable information and constructing intelligence algorithms who can "learn" since the data and make optimal decisions for classification or forecasting tasks.

This course is focused on practical approach, so i'll supply you useful snippet codes and i'll teach you how to build professional desktop applications for machine learning and datamining with python language.
We'll also manage real data from an example of a real trading company and presenting our results in a professional view with very illustrated graphical charts.
We'll initiate at the basic level covering the main topics of Python Language and also the needing programs to develop our applications. We will make a review of the main packages for scientific use and data analysis in python such us Numpy, Pandas, Matplotlib, Seaborn, Scikit-Learn and more. After that we'll dive into maching learning models applying the very powerful Scikit-Learn package, but also we will construct our own code and interpretations.
Hot topics on Machine Learning and Data Mining that we will cover with practical applications on this course are:

  1. - Data Analysis and graphical display.
  2. - Linear and Multiple Regression
  3. - Regularization
  4. - Polynomial Regression
  5. - Logistic Regression
  6. - Cross Validation
  7. - Support Vector Machines for Regression and Classification
  8. - Decision Trees and Random Forest
  9. - KNN algorithm
  10. - GridSearchCV
  11. - Principal Component Analysis (PCA)
  12. - Linear Discriminant Analysis (LDA)
  13. - Kernel Principal Component Analysis (KPCA)
  14. - Ensemble methods
  15. - K means clustering analysis
  16. - Market Basquet Analysis
  17. - Time Series with ARIMA models
  18. - Gradient Descent
  19. - Multilayer Neural Networks

We will also work with MySql database, presenting data through Graphical User Interface (GUI), on windows, tables, labels, textboxs, interacting with buttons, combo box, mouse events and much more.

Online Course CoupoNED
Online Course CoupoNED I am very happy that there are bloggers who can help my business

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