Python for Machine Learning and Data Mining
4.1 (6 ratings)
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Python for Machine Learning and Data Mining

Practical Approach
Best Seller
4.1 (6 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
67 students enrolled
Created by CARLOS QUIROS
Last updated 9/2017
English
English [Auto-generated]
Current price: $12 Original price: $120 Discount: 90% off
3 days left at this price!
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Includes:
  • 12 hours on-demand video
  • 1 Article
  • 12 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I 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
View Curriculum
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.

Who is the target audience?
  • 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
Compare to Other Machine Learning Courses
Curriculum For This Course
81 Lectures
12:00:32
+
Python Basics
15 Lectures 02:20:38

Environment Set Up: Anaconda
09:56

Jupyter Notebook
06:19

Sublime Text Editor
07:11

Data Types and Operations - Part 1
12:43

Data Types and Operations - Part 2
10:22

Data Types and Operations - Part 3
11:33

Statements and Handling Errors - Part 1
13:43

Statements and Handling Errors - Part 2
06:34

Functions - Part 1
10:11

Functions - Part 2
08:52

Persistance
07:12

Object Oriented Programming (OOP) in Python
09:34

Exercises for Python Basics
04:37

Exercises for Python Basics - Solutions
18:49
+
Data Analysis
16 Lectures 02:29:45
Introduction
01:02

Numpy
09:13

Pandas: Data Structures and Operations
17:29

Pandas: Applying Functions
07:11


Pandas: Merge, Join and Concatenate Data
09:07

Statistics with Pandas
04:12

Pivot Tables with Pandas
05:46

Matplotlib Concepts
13:06

Statistical graphs with matplotlib
13:47

Bonus Class: Animation on Matplotlib
05:28

Seaborn on Categorical Data
11:27

Seaborn on Numerical Data
11:36

Seaborn on Regression
09:31

Data Analysis Exercises
05:32

Data Analysis Exercises: Solutions
18:22
+
MySQL with Python
6 Lectures 41:28
MySQL workbench installation and setup
04:25

Creating a database and tables in MySQL
06:19

CRUD operations: review of SQL language
08:10


Pandas and PyMySQL
06:23

MySQL with Python: Exercises and Solutions
07:58
+
GUI on Python
6 Lectures 43:24
PyQT5 and Qt Designer Installation and first template
04:31

Access to properties of elements by code
09:40

Open dialog from Main Window
06:31

Combo Box, Radio Button and Check Box
09:27

Menu Bar
06:06

QTableWidget and connection to MySQL database
07:09
+
Machine Learning and Data Mining with Python
25 Lectures 03:30:44
Introduction to Machine Learning and Data Mining
12:36

Machine Learning concepts: Overfitting, Underfitting, Cross Validation
07:35

Scikit-Learn
03:29

Regression model and evaluation: R-Squared, RMSE and MAE
06:39

Single and Multiple Linear Regression
10:07

Regularization: Lasso, Ridge and ElasticNet
09:09

Support Vector Machines for Regression
04:07

Polynomial Regression
05:25

Classification model and evaluation: Confusion Matrix, ROC Curve
06:15

Logistic Regression
09:55

Plotting Decision Boundaries
12:01

Support Vector Machines for Classification
09:16

Decision Trees and Random Forest
14:18

K Nearest Neighbors (KNN)
05:41

Hyperparameter optimization: GridSearchCV
06:42

Principal Component Analysis (PCA)
09:43

Linear Discriminant Analysis (LDA)
07:43

Kernel Principal Component Analysis (KPCA)
09:50

Ensemble Methods: Bagging (Bootstrap aggregation)
10:31


K Means Clustering
07:54

Neural Networks: Perceptron
11:24

Neural Networks: Multilayer Perceptron
06:01

Machine Learning Exercises
04:08

Machine Learning Exercises: Solutions
11:44
+
Creating a Machine Learning Desktop Application
13 Lectures 02:14:33
Designing GUI, Case: Medical Supplies Distributor Company
09:58

Data Analysis and Data Visualization: Analysing Sales
17:22

Regression application for Sales: Multivariate model-part 1
15:35

Regression application for Sales: Multivariate model-part 2
13:23

Classification application for Credit Policy
13:46

Market Basquet Analysis: A-priori algorithm and metrics
07:35

Market Basket Analysis application: MBA code in Python
12:12

Time Series Concepts
06:55

Time Series with Python: Arima and Sarimax models
11:52

Classification application with Neural Nets
08:51


Recommendations
04:59

You can access to this course with ONLY USD10.00 using this coupon: PYTHONML10 

The offer is limited to the first 100 students. Enjoy the content and improve your career!!

Preview 00:09
About the Instructor
CARLOS QUIROS
4.1 Average rating
6 Reviews
67 Students
1 Course
Industrial Engineer and Data Scientist

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), and others specializations like Total Quality Managment.
He is also an experience programmer specialized on .NET systems, PHP, Java, R, Databases and Python, applying his experience on machine learning and data mining models developing applications in many fields of the industry and service like marketing, planning inventory, planning sales, finance, quality control, computer vision and so on.
He wants to share his experience teaching you on a simple and practical way, illustrating concepts based on graphics for better understanding.