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, soi'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:

- Data Analysis and graphical display.
- Linear and Multiple Regression
- Regularization
- Polynomial Regression
- Logistic Regression
- Cross Validation
- Support Vector Machines for Regression and Classification
- Decision Trees and Random Forest
- KNN algorithm
- GridSearchCV
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Kernel Principal Component Analysis (KPCA)
- Ensemble methods
- K means clustering analysis
- Market Basquet Analysis
- Time Series with ARIMA models
- Gradient Descent
- 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.

The student has the opportunity to get a feedback from the instructor through Q&A forums, by email: [email protected] or by Twitter: @AILearningCQ