Python-Introduction to Data Science and Machine learning A-Z

Python basics Learn Python for Data Science Python For Machine learning and Python Tips and tricks

4.37 (3290 reviews)
Udemy
platform
English
language
Data Science
category
instructor
289,672
students
7.5 hours
content
Feb 2024
last update
$49.99
regular price

What you will learn

Uderstand the basics of python programming

learning all the basic mathematical concepts

Understand the basics of Data science and how to perform it using Python

Learn to use different python tools specialisez for data science

Improve your python programming by integrating new concepts

Learning the basics of Machine learning

Perform various analysis with sklearn

Finish the course with a complete understand of all the core concepts of Data science and all the required tools to perform it with python

Description

Learning how to program in Python is not always easy especially if you want to use it for Data science. Indeed, there are many of different tools that have to be learned to be able to properly use Python for Data science and machine learning and each of those tools is not always easy to learn. But, this course will give all the basics you need no matter for what objective you want to use it so if you :

- Are a student and want to improve your programming skills and want to learn new utilities on how to use Python

- Need to learn basics of Data science

- Have to understand basic Data science tools to improve your career

- Simply acquire the skills for personal use

Then you will definitely love this course. Not only you will learn all the tools that are used for Data science but you will also improve your Python knowledge and learn to use those tools to be able to visualize your projects.

The structure of the course

This course is structured in a way that you will be able to to learn each tool separately and practice by programming in python directly with the use of those tools. Indeed, you will at first learn all the mathematics that are associated with Data science. This means that you will have a complete introduction to the majority of important statistical formulas and functions that exist. You will also learn how to set up and use Jupyter as well as Pycharm to write your Python code. After, you are going to learn different Python libraries that exist and how to use them properly. Here you will learn tools such as NumPy or SciPy and many others. Finally, you will have an introduction to machine learning and learn how a machine learning algorithm works. All this in just one course.

Another very interesting thing about this course it contains a lot of practice. Indeed, I build all my course on a concept of learning by practice. In other words, this course contains a lot of practice this way you will be able to be sure that you completely understand each concept by writing the code yourself.

For who is this course designed

This course is designed for beginner that are interested to have a basic understand of what exactly Data science is and be able to perform it with python programming language. Since this is an introduction to Data science, you don't have to be a specialist to understand the course. Of course having some basic prior python knowledge could be good but it's not mandatory to be able to understand this course. Also, if you are a student and wish to learn more about Data science or you simply want to improve your python programming skills by learning new tools you will definitely enjoy this course. Finally, this course is for any body that is interested to learn more about Data science and how to properly use python to be able to analyze data with different tools.

Why should I take this course

If you want to learn all the basics of Data science and Python this course has all you need. Not only you will have a complete introduction to Data science but you will also be able to practice python programming in the same course. Indeed, this course is created to help you learn new skills as well as improving your current programming skills.

There is no risk involved in taking this course

This course comes with a 100% satisfaction guarantee, this means that if your are not happy with what you have learned, you have 30 days ​to get a complete refund with no questions asked. Also, if there is any concept that you find complicated or you are just not able to understand, you can directly contact me and it will be my pleasure to support you in your learning.

This means that you can either learn amazing skills that can be very useful in your professional or everyday life or you can simply try the course and if you don't like it for any reason ask for a refund.

You can't lose with this type of offer !!


ENROLL NOW and start learning today :)

Content

Introduction

Introduction
What is Data Science
Installation of Anaconda and Jupyter
Introduction to Jupyter Part 1
Introduction to Jupyter Part 2

Basic Statstics knowledge

The Basics of Data
The basics of statistics part 1
The basics of statistics part 2
The basics of statistics part 3
The basics of statistics part 4
The basics of statistics part 5
The basics of statistics part 6

Python library: NumPy

Introduction to Numpy
Setting up NumPy
Basic calculations Part 1
Basic calculations Part 2
Basic calculations Part 3
Basic calculations Part 4
Basic calculations Part 5

Python library: Pandas

The Basics of Pandas
Setting up Pandas
Pandas operations part 1
Pandas operations part 2
Pandas operations part 3
Pandas operations part 4
Pandas operations part 5

Python library: Scipy

The Basics of SciPy
SciPy operations part 1
SciPy operations part 2
SciPy operations part 3
SciPy operations part 4
SciPy operations part 5

Python library : Matplotlib

Introduction to Matplotlib
Setting up MatPlotlib
Basics of matplotlib part 1
Basics of matplotlib part 2
Basics of matplotlib part 3
Basics of matplotlib part 4
Basics of matplotlib part 5

Python library: Seaborn

Introduction to Seaborn
Setting up seaborn
Seaborn operations part 1
Seaborn operations part 2
Seaborn operations part 3
Seaborn operations part 4

Machine Learning

Introduction to machine learning
Presentation of Different algorithms
Machine learning algorithms part 1
Machine learning algorithms part 2
Machine learning algorithms part 3

Conclusion

Conclusion

Screenshots

Python-Introduction to Data Science and Machine learning A-Z - Screenshot_01Python-Introduction to Data Science and Machine learning A-Z - Screenshot_02Python-Introduction to Data Science and Machine learning A-Z - Screenshot_03Python-Introduction to Data Science and Machine learning A-Z - Screenshot_04

Reviews

Baldric
November 8, 2023
it was actually very informative and showed me what to prepare for. You may not be a master at the end of this course but atleast you'll know the direction to go to achieve mastery
Alex
September 13, 2023
Besides going over some minor theory without proper explanations (which seemed like the author reiterated the first paragraph of wikipedia entries for each topic), the API explanations, coding conventions and given suggestions are not up to par with what they claim to teach. Seeing the code instead of running a debugger to find where the issue happens, bad naming practices, bad coding practices, and many more. All in all, not recommended. Better off watching one of the many freeware courses from big universities, available in YouTube or other platforms.
Salman
July 30, 2023
iTS A GOOD COURSE WITH BASICS AND ALL BUT AS PER THE NAME I FELT AS IF IT DID NOT INCLUDE ENOUGH MATERIAL.
Melisa
July 28, 2023
The course is for people who already have knowledge of this topic, because for beginners (like me) there is a lot of things that Yassin is not explaining and have to search to understand a little bit more, but sometimes is impossible to get the idea or the why he is coding with those functions/modules/tools and not with others, not the best course I had...
John
July 19, 2023
Stopped as soon as the presenter used 'problematic' when they mean problem or problem space. If you can't use the right fundamental terminology, not going to assume any other information they are imparting is correct. If it were a term of art (it isn't) then they should call that out.
Dara
July 12, 2023
This is a good course for anyone, who already knows how to program in Python, to get some preliminary background on statistics, Jupyter Notebook, and Python libraries NumPy, Pandas, SciPy, Matplotlib, Seaborn, and Scikit-Learn, and some very high-level background on machine learning (excluding anything to do with neural networks, as these are not covered in any way) so that you can then spend your own time on researching these and playing with them. The course leaves quite a bit to be desired, however, and can be difficult to follow, if you actually want to take some notes or try out the coding examples yourself. As the course doesn't seem to have been planned with a clear transcript before the trainer recorded it (or maybe the trainer doesn't actually know the subject as well as they should), the trainer tends to not explain things very well and waffles a lot (e.g. talks about how things are "simple" or how things are "basically complicated", and contradicting themselves about whether Seaborn is better than Matplotlib when introducing Seaborn), uses some incorrect terminology (e.g. calling colons two-dots, probably due to a literal translation from French, and referring to output of functions as functions, rather than variables/objects), and makes mistakes in their code and imports libraries that don't actually need to be imported. Sadly, except for explaining how to install Python libraries though PyCharm, the trainer provides no information on how else this could be done and makes no references to official documentation of the libraries being used during demonstrations to explain why the code is being written in the way that it is being written (e.g. why certain arguments are provided to certain functions). So, if you have any level of curiosity, you will most likely have to keep pausing the videos to search for online documentation and other examples to understand why the trainer is writing their code the way that they're writing it and get a much better understanding through your own research. Due to inadequate planning, there is no overarching demonstration of how all of the tools and libraries introduced earlier in the course come together (except a few such examples near the end of the course). For example, Jupyter Notebooks is introduced very early in the course, but none of the coding examples actually make use of it, which leaves us wondering why it was even mentioned in the first place. The coding exercises are additionally difficult to follow because the maximum video resolution of the course videos is 720p, but the trainer's screen was running at a higher resolution when the videos were recorded, so the code in the video is rather difficult to read due to blurring caused by down-sampling of the original higher resolution video.
Mokhele
January 20, 2023
What an engaging course. The instructor is very clear and elaborate on concepts. I recommend this course to anyone serious about data science
Sze
January 2, 2023
Very little explanation to the concept especially to the Machine Learning part. Do not understand the latter part even after I finished the course.
Zahra
December 5, 2022
really helped me. I had a data analysis course where I was completely oblivious in Python, took the course, put in on 1.5 speed, and in 5 hours I was able to understand everything I was missing within the course. really helpfull!
Rafał
November 25, 2022
I found Corey Schafer Youtube course about Python, Matplotlib and Maching Learning way more informative and better presented.
Afnan
August 7, 2022
A very excellent course, especially for beginners, because it provides a simplified explanation of each paragraph, and the best courses are in which the lecturer writes the code with you, thank you very much
SOMDA
June 17, 2022
Oui les explications sont claires. Des exemples parlants sont pris et des possibilités d'applications des connaissances acquises sont données
Sai
June 15, 2022
Everything in this course is pretty good and interesting. Recommended course for beginners who wish to get started with ds and ml. I wish that you could have explained the code in detail in Section 8.
Nasiru
March 21, 2022
quite insightful but we would need better explanation on executing the python code and how to debug errors
Tanya
February 17, 2022
Course is ok but the dark mode used makes the lecture code difficult to read. Use the IDE in a more high contrast mode.

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Related Topics

3105814
udemy ID
5/9/2020
course created date
9/3/2020
course indexed date
Lee Jia Cheng
course submited by