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

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Python-Introduction to Data Science and Machine learning A-Z [TutsNode.com] - Python-Introduction to Data Science and Machine learning A-Z 1. Introduction
  • 2. What is Data Science.mp4 (262.9 MB)
  • 2. What is Data Science.srt (32.6 KB)
  • 5. Introduction to Jupyter Part 2.srt (14.8 KB)
  • 1. Introduction.srt (11.9 KB)
  • 4. Introduction to Jupyter Part 1.srt (10.1 KB)
  • 3. Installation of Anaconda and Jupyter.srt (5.1 KB)
  • 1. Introduction.mp4 (101.6 MB)
  • 5. Introduction to Jupyter Part 2.mp4 (35.2 MB)
  • 4. Introduction to Jupyter Part 1.mp4 (30.8 MB)
  • 3. Installation of Anaconda and Jupyter.mp4 (26.6 MB)
8. Machine Learning
  • 2. Presentation of Different algorithms.srt (28.8 KB)
  • 4. Machine learning algorithms part 2.srt (27.2 KB)
  • 2. Presentation of Different algorithms.mp4 (243.2 MB)
  • 1. Introduction to machine learning.srt (16.9 KB)
  • 5. Machine learning algorithms part 3.srt (13.0 KB)
  • 3. Machine learning algorithms part 1.srt (11.9 KB)
  • 4. Machine learning algorithms part 2.mp4 (162.5 MB)
  • 1. Introduction to machine learning.mp4 (132.0 MB)
  • 5. Machine learning algorithms part 3.mp4 (63.5 MB)
  • 3. Machine learning algorithms part 1.mp4 (34.2 MB)
2. Basic Statstics knowledge
  • 3. The basics of statistics part 2.srt (23.3 KB)
  • 7. The basics of statistics part 6.srt (21.9 KB)
  • 1. The Basics of Data.srt (16.6 KB)
  • 6. The basics of statistics part 5.srt (15.7 KB)
  • 5. The basics of statistics part 4.srt (15.3 KB)
  • 2. The basics of statistics part 1.srt (13.6 KB)
  • 4. The basics of statistics part 3.srt (8.9 KB)
  • 3. The basics of statistics part 2.mp4 (192.9 MB)
  • 7. The basics of statistics part 6.mp4 (172.2 MB)
  • 1. The Basics of Data.mp4 (124.3 MB)
  • 5. The basics of statistics part 4.mp4 (117.2 MB)
  • 6. The basics of statistics part 5.mp4 (114.9 MB)
  • 2. The basics of statistics part 1.mp4 (110.0 MB)
  • 4. The basics of statistics part 3.mp4 (66.3 MB)
6. Python library Matplotlib
  • 3. Basics of matplotlib part 1.srt (18.6 KB)
  • 5. Basics of matplotlib part 3.srt (13.7 KB)
  • 4. Basics of matplotlib part 2.srt (10.0 KB)
  • 6. Basics of matplotlib part 4.srt (9.6 KB)
  • 1. Introduction to Matplotlib.srt (9.4 KB)
  • 7. Basics of matplotlib part 5.srt (7.0 KB)
  • 2. Setting up MatPlotlib.srt (2.1 KB)
  • 1. Introduction to Matplotlib.mp4 (70.4 MB)
  • 3. Basics of matplotlib part 1.mp4 (61.6 MB)
  • 5. Basics of matplotlib part 3.mp4 (41.5 MB)
  • 4. Basics of matplotlib part 2.mp4 (33.5 MB)
  • 6. Basics of matplotlib part 4.mp4 (31.0 MB)
  • 7. Basics of matplotlib part 5.mp4 (24.9 MB)
  • 2. Setting up MatPlotlib.mp4 (7.6 MB)
3. Python library NumPy
  • 5. Basic calculations Part 3.srt (14.9 KB)
  • 4. Basic calculations Part 2.srt (14.4 KB)
  • 6. Basic calculations Part 4.srt (12.0 KB)
  • 1. Introduction to Numpy.srt (9.8 KB)
  • 3. Basic calculations Part 1.srt (9.2 KB)
  • 7. Basic calculations Part 5.srt (6.5 KB)
  • 2. Setting up NumPy.srt (5.1 KB)
  • 1. Introduction to Numpy.mp4 (83.5 MB)
  • 5. Basic calculations Part 3.mp4 (46.4 MB)
  • 4. Basic calculations Part 2.mp4 (41.5 MB)
  • 6. Basic calculations Part 4.mp4 (36.1 MB)
  • 3. Basic calculations Part 1.mp4 (26.4 MB)
  • 7. Basic calculations Part 5.mp4 (18.7 MB)
  • 2. Setting up NumPy.mp4 (16.6 MB)
7. Python library Seaborn
  • 3. Seaborn operations part 1.srt (14.2 KB)
  • 5. Seaborn operations part 3.srt (13.2 KB)
  • 6. Seaborn operations part 4.srt (10.2 KB)
  • 1. Introduction to Seaborn.srt (8.7 KB)
  • 4. Seaborn operations part 2.srt (8.5 KB)
  • 2. Setting up seaborn.srt (2.6 KB)
  • 1. Introduction to Seaborn.mp4 (66.6 MB)
  • 3. Seaborn operations part 1.mp4 (53.7 MB)
  • 5. Seaborn operations part 3.mp4 (43.6 MB)
  • 6. Seaborn operations part 4.mp4 (35.4 MB)
  • 4. Seaborn operations part 2.mp4 (32.4 MB)
  • 2. Setting up seaborn.mp4 (11.4 MB)
4. Python library Pandas
  • 5. Pandas operations part 3.srt (13.4 KB)
  • 1. The Basics of Pandas.srt (10.3 KB)
  • 6. Pandas operations part 4.srt (9.6 KB)
  • 3. Pandas operations part 1.srt (8.9 KB)
  • 7. Pandas operations part 5.srt (8.8 KB)
  • 4. Pandas operations part 2.srt (5.5 KB)
  • 2. Setting up Pandas.srt (3.9 KB)
  • 1. The Basics of Pandas.mp4 (89.0 MB)
  • 5. Pandas operations part 3.mp4 (44.9 MB)
  • 6. Pandas operations part 4.mp4 (38.1 MB)
  • 7. Pandas operations part 5.mp4 (34.4 MB)
  • 3. Pandas operations part 1.mp4 (28.9 MB)
  • 4. Pandas operations part 2.mp4 (19.9 MB)
  • 2. Setting up Pandas.mp4 (11.9 MB)
5. Python library Scipy
  • 6. SciPy operations part 5.srt (12.2 KB)
  • 2. SciPy operations part 1.srt (12.1 KB)
  • 3. SciPy operations part 2.srt (11.9 KB)
  • 4. SciPy operations part 3.srt (11.4 KB)
  • 5. SciPy operations part 4.srt (8.6 KB)
  • 1. The Basics of SciPy.srt (8.2 KB)
  • 1. The Basics of SciPy.mp4 (62.8 MB)
  • 2. SciPy operations part 1.mp4 (35.3 MB)
  • 6. SciPy operations part 5.mp4 (30.3 MB)
  • 3. SciPy operations part 2.mp4 (28.5 MB)
  • 4. SciPy operations part 3.mp4 (27.8 MB)
  • 5. SciPy operations part 4.mp4 (19.9 MB)
9. Conclusion
  • 1. Conclusion.srt (3.7 KB)
  • 1. Conclusion.mp4 (28.1 MB)
  • TutsNode.com.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)
    • .pad
    • 0 (1.5 KB)
    • 1 (273.9 KB)

Description


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
Who this course is for:

Beginner python users curious about Data Science

Requirements

Strong motivation to learn new skills
Basic python programming skills (can be helpful but not mandatory)

Last Updated 9/2020



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

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3.2 GB
seeders:15
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Udemy - Python-Introduction to Data Science and Machine learning A-Z

Torrent hash: 5B58543DFCD917E492996BAAA2EEEE4E6817F265