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Development Data Science Data Mining

Python for Machine Learning and Data Mining

Numpy, Pandas, Matplotlib, Seaborn, Neural Networks, Time Series, Market Basquet Analysis, GUIs, MySQL and much more!!
Rating: 3.6 out of 53.6 (94 ratings)
857 students
Created by CARLOS QUIROS
Last updated 7/2019
English
English
30-Day Money-Back Guarantee

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

Course content

7 sections • 83 lectures • 12h 24m total length

  • Preview03:02
  • Environment Set Up: Anaconda
    09:51
  • 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

  • Introduction
    01:02
  • Numpy
    09:13
  • Pandas: Data Structures and Operations
    17:29
  • Pandas: Applying Functions
    07:11
  • Preview06:56
  • 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 workbench installation and setup
    04:25
  • Creating a database and tables in MySQL
    06:19
  • CRUD operations: review of SQL language
    08:10
  • Preview08:13
  • Pandas and PyMySQL
    06:23
  • MySQL with Python: Exercises and Solutions
    07:58

  • 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

  • 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
  • Preview08:31
  • 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

  • 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
  • Regression application: Modeling a function with MLP
    11:56
  • Recommendations
    04:59
  • Lessons, Codes and Datasets
    00:06

  • Jupyter Notebook Extensions-part1
    10:31
  • Jupyter Notebook Extensions part2
    13:05

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 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
Industrial Engineer and Data Scientist
CARLOS QUIROS
  • 4.0 Instructor Rating
  • 1,095 Reviews
  • 9,105 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. 

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