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Machine Learning, Data Science and Deep Learning with Python , name of the training video series in the field of machine learning, science, data, and deep learning with Python can be. Period, you as a comprehensive course for the complete training of machine learning with knowledge, data, etc. Tensorflow, etc., artificial intelligence, and neural networks can be. You are viewing the course video to be able to build artificial neural networks with Tensorflow and Keras, you will. On the other hand, you\\\\\\\’ll learn to deep learning, etc. images. data automatically, and with the use of artificial intelligence classification, the.

Set before you also give you the techniques and skills quite practical will teach. You, as well as a thorough classification of data using K-Means to learn. Also training, this collection somehow have been prepared that you view and analyze it to understand the very desirable from a machine learning (Machine Learning) hands you\\\\\\\’ll find.

Technical course in Machine Learning, Data Science and Deep Learning with Python

You\\\\\\\’re students, dear, with, view and learn the concepts, skills and techniques available in the training set by machine learning from scratch will be familiar. Also, you complete with the application of the training provided in the training set will be familiar. Also, you, the students respected, for finishing the course, video, etc. 91 the meeting Training Video On Your have.

Profile courses:

  • Publisher: Udemy
  • Instructor: Sundog Education by Frank Kane Frank Kane
  • Level: introductory to advanced
  • Duration Time: 14:14:31
  • Number of courses: 111 classroom
  • Language: English

Topics in Machine Learning, Data Science and Deep Learning with Python

Course content
111 lectures 14:14:31

Getting Started
11 lectures 01:00:23

Statistics and Probability Refresher, and Python Practice
13 lectures 02:02:16

Predictive Models
4 lectures 35:07

Machine Learning with Python
16 lectures 01:39:00

Recommender Systems
6 lectures 49:10

More Data Mining and Machine Learning Techniques
9 lectures 01:17:39

Dealing with Real-World Data
10 lectures 01:11:47

Apache Spark: Machine Learning on Big Data
12 lectures 01:32:59

Experimental Design / ML in the Real World
6 lectures 41:58

Deep Learning and Neural Networks
19 lectures 03:03:19

Final Project
2 lectures 16:45

You made it!
3 lectures 04:06


  • You\\\\\\\’ll need a desktop computer (Windows, Mac, or Linux) capable of running Enthought Canopy 1.6.2 or newer. The course will walk you through installing the necessary free software.
  • Some prior coding or scripting experience is required.
  • At least high school level math skills will be required.
  • This course walks through getting set up on a Microsoft Windows based desktop PC. While the code in this course will run on other operating systems, we cannot provide OS-specific support for them.

Images, Machine Learning, Data Science and Deep Learning with Python

Machine Learning, Data Science and Deep Learning with Python

Sample movie

Installation guide

After the Extract with the Player your custom view.

The previous title of this tutorial, Data Science, Deep Learning & Machine Learning with Python has been.

This tutorial has subtitles in English.

Quality Movies, 720p is.

This tutorial is Downloadly purchased. In case of delay in providing updates, New, etc. to remind us get.


Version 2019/5 compared to 2019/4 certain changes had been. Apparently reforms have been.

Version 2019/6 compared to 2019/5 number of 9 Lessons and 1 hour time has increased.

Version 2019/10 compared to 2019/6 number of the 8 lessons, and 50 minutes increased.

Version 2019/11 compared to the version 2019/10 the number of a classroom and 1 minute increased.

Version 2019/12 compared to 2019/11 only 3 H, declined and for that reason is not updated yet.

Version 2020/2 compared to 2019/12 the number of 1 lesson and about 20 minutes has increased.

Version 2020/5 compared to 2020/2 a minute, decline that result the change in current headings have been.

Version 2020/9 has not changed in number of courses and time compared to 2020/5 and has not been updated.

Download link

Download Part 1 – 2 GB

Download Part 2 – 2 GB

Download Part 3 – 2 GB

Download Part 4 – 1.30 GB

Password file(s):

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File size

7.33 GB

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