Python for Machine Learning and Data Mining (Udemy.com)

Numpy, Pandas, Matplotlib, Seaborn, Neural Networks, Time Series, Market Basquet Analysis, GUIs, MySQL and much more!!

( 14 Reviews )

Created by: CARLOS QUIROS

Produced in 2021

What you will 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

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 84 / 100

Course 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

Time: 12.5h

Instructor Details

CARLOS QUIROS

4.2 Rating
14 Reviews

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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Reviews

4.2

14 total reviews

5 star 4 star 3 star 2 star 1 star

Write a Review

By Saidivya Bhallamudi

good but need more clarity in pronunciation

By Varun Sethi

So far so good..most concise and accurate explanation of concepts

By Khemiri monem

I hoped you tell us more -in short- about what is a package ? what is a distribution of a programming language ?

By Juan Carlos Martinez Santos

Great tips on how to use Jupiter.

By Carlos

Very well explained, with detail. It's a very comprehensive course on machine learning with good reviews of python, and its libraries.

By Vader S

I am going through this course and will update this review.

Update: I have completed 28 out of the 81 lectures.

Pros:

1-The course if very concise and covers a lot.

2-several topics a re covered from data visualization to analysis

3-The instructor is responsive and answers questions.

Cons:

1-It would be nice if all lectures are subtitled in English for clarity.

2-some topics require more clarification

I will update this course as I go along.

By Sam Yin

some of the function need more detail explanation. The instructor does not have to cover too many functions with similar effect. But for some important function, the instructor may give more explanation. However, the instructor is always there for help.~

By Praveen M Dhulavvagol

exposure to python and Machine Learning .

Real world applications on Machine learning

Neural Network working and applications

programming exercises using python

By Sheeri Cabral

I like that the course has practice exercises. So far I'm only in the setup but the instructor is clear and I was able to set things up with no problems.

By Ashank Gupta

The course content skips some of the important theoretical aspects in favor of going over the code. I feel the theory should have been more focused on at least in chapter 5 (Machine Learning). Besides I would have liked to use one dataset for all kinds of algorithm which would enable a contrast and compare approach.

By Goede

Good instructor. He explains everything in detail.

Lessons/Chapter are setup on a top/down style:

1-Python intro

2-MySQL DB

3-GUI Design

4-Machine Learning

Gives practical/real world examples.

By Tolulope Odetola

The teacher does not explain in detail. This course is easier to understand if you are already familiar with Matlab. Eg. in the last lecture he did not explain the new axis concept clearly!!!!