Python pandas tutorial | road to machine learning part 3

Focused View

Michal Hucko

2:14:25

8 View

1.Introduction.mp4

01:41

2.Reading.and.attributes.mp4

20:27

3.Selecting.mp4

15:35

4.Indexing.mp4

11:09

5.Multi.index.columns.mp4

05:34

6.Updating.mp4

09:02

7.Joining.mp4

09:04

8.Describe.mp4

19:51

9.Iterating.mp4

05:38

10.Group.by.mp4

14:20

11.Strings.datetimes.sort.mp4

08:45

12.Ploting.mp4

07:12

13.Writing.dataframe.mp4

03:32

14.Project.mp4

02:35

Description

In this tutorial I teach pandas essentials. Pandas is a python library dedicated for data analysis. Is one of the most important tools of the modern machine learning developers. In the tutorial we cover all the functionality needed to head-start the data-analysis in pandas.

For the whole course we prepared detailed examples documented and executed in the jupyter lab notebooks. You can find source code on the course github. We highly recommend first to try to code the examples along the lecture. Use the source code as a last resource to overcome the errors. This way you will learn much more with the course :).

During the lessons we present example code run on the kaggle dataset which you can find on this link. To download the dataset from the kaggle you need to be registered. Registration is free. Kaggle is an ideal place for datasets dedicated for learning the machine learning :).

The agenda of the course is following:

Reading the dataframe and accessing attributes
Selecting from the dataframe
Indexing the dataframe
Column multi index
Updating the datframe
Joining the dataframes
Describing the dataframe (intro to exploratory analysis)
Iterating the dataframe
Group by
String, dates and sort
Basics of plotting
Writing the dataframe

What You'll Learn?

Technology
Python
Statistics
Big Data
Data Science
Machine Learning
Pandas

More details

User Reviews

Rating

average 0

Total votes0

Focused display

Python

Machine Learning

Pandas

Michal Hucko

Instructor's Courses

Hello world!! My name is Michal Hucko and I am passionate python developer. I am former university teacher. I was doing my Phd degree in computer science, however because of unfortunate situation I decided to currently postpone the study. Thats why I want to teach computer science online. Hope I can help you to understand the modern world of machine learning and distributed computing.

Besides programming I like to spent time with my wife, my brother and my friends. I am passionate fitness guy and sometimes I play computer games.

About my engineering career

For past 5 years I am working as a machine learning dev ops developer. I am working mostly with docker, kubernetes and python. Currently I am working for one of the biggest computer company in the world :). Feel free to check out my Linkedin profile for complete cv.

About my machine learning research

I started my machine learning career with text processing. More specifically natural language processing and text classification. For the last 4 years I aimed my research to the affective computing. Within this field I was predicting user emotion based on mouse movements using artificial intelligence. I am author of couple of research papers in the field of affective computing and text classification

I want to provide variety of courses starting from those for beginners up to the professionals. Feel free to contact me anytime you want :)

About my classes

At the university i taught basics of procedural programming and basics of object oriented programming. Here on skillshare I want to cover the whole package for absolute beginners in machine learning:

I want to cover:

Python basics
Object oriented python
Jupyter notebooks
Python basics of data analysis
Python basics in machine learning
Docker basics
Basics of distributed computing
And many more

I am here for beginners who want to explore the beauties of modern machine learning. :)

SkillShare

View courses SkillShare

Skillshare is an online learning community based in the United States for people who want to learn from educational videos. The courses, which are not accredited, are only available through paid subscription.