Python for Machine Learning and Data Mining
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:
- 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: machine.learning.eirl@gmail.com or by Twitter: @AILearningCQ
Who this course is for:
- Everyone who wants to learn about machine learning and datamining concepts and applications
- People who want to start or improve their careers as a data scientist
- People who wants to programm in python language
Instructor
![CARLOS QUIROS](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.2 Instructor Rating
- 1,241 Reviews
- 10,142 Students
- 6 Courses
Industrial Engineer with more than 20 years in developing and managing business, with vast experience on process analysis and developing business information systems for data science. He has an Industrial Engineering degree from Pontificia Universidad Catolica del Peru (Lima-Peru) and Master in Business Administration (MBA) from ESAN Graduated School of Business (Lima-Peru).
He is also an experience developer of machine learning and data science models in many fields of the industry and services like Marketing, Logistics, Finance, Manufacture, Quality Control, Computer Vision, NLP, Deep Learning apps and many others.
He wants to share his experience teaching you on a simple and practical way, illustrating concepts based on graphics for better understanding.