Udemy - Machine Learning & Data Science A-Z Hands-on Python 2021

seeders: 7
leechers: 8
updated:
Download torrent
Added by notmrME in Other > Tutorials
Download Fast Safe Anonymous
movies, software, shows...

Files

Machine Learning & Data Science A-Z Hands-on Python 2021
  • Downloaded from 1337x.txt (0.0 KB)
    • 06 Supervised Learning - Regression
    • 008 Random Forest Model Development.mp4 (246.2 MB)
    • 001 Simple and Multiple Linear Regression Concepts.mp4 (212.2 MB)
    • 002 Multiple Linear Regression - Model Development.mp4 (75.6 MB)
    • 003 Evaluation Metrics - Concepts.mp4 (49.5 MB)
    • 004 Evaluation Metrics - Implementation.mp4 (159.9 MB)
    • 005 Polynomial Linear Regression Concepts.mp4 (26.4 MB)
    • 006 Polynomial Linear Regression Model Development.mp4 (219.1 MB)
    • 007 Random Forest Concepts.mp4 (30.2 MB)
    • 009 Support Vector Regression Concepts.mp4 (27.0 MB)
    • 010 Support Vector Regression Model Development.mp4 (121.0 MB)
    01 Introduction
    • 002 What is Machine Learning_ Some Basic Terms.mp4 (25.8 MB)
    • 003 Python Installation.html (1.5 KB)
    • 004 Python IDE.mp4 (7.5 MB)
    • 005 IDE Installation.mp4 (22.3 MB)
    • 006 Installation of Required Libraries.mp4 (70.8 MB)
    • 007 Spyder Interface.mp4 (46.4 MB)
    • 001 Course Content.mp4 (17.1 MB)
    02 Machine Learning Useful Packages (Libraries)
    • 001 Python Source Codes.html (1.2 KB)
    • 002 NumPy1.mp4 (37.5 MB)
    • 003 NumPy2.mp4 (56.9 MB)
    • 004 NumPy3.mp4 (84.5 MB)
    • 005 NumPy4.mp4 (56.6 MB)
    • 006 NumPy5.mp4 (152.6 MB)
    • 007 NumPy6.mp4 (134.5 MB)
    • 008 Pandas1.mp4 (95.6 MB)
    • 009 Pandas2.mp4 (116.9 MB)
    • 010 Pandas3.mp4 (117.8 MB)
    • 011 Pandas4.mp4 (203.0 MB)
    • 012 Visualization with Matplotlib1.mp4 (99.4 MB)
    • 013 Visualization with Matplotlib2.mp4 (205.2 MB)
    • 014 Visualization with Matplotlib3.mp4 (188.8 MB)
    • 015 Visualization with Matplotlib4.mp4 (143.0 MB)
    • 016 Visualization with Matplotlib5.mp4 (129.2 MB)
    • 018 Data_Set.txt (0.6 KB)
    • Readme.txt (0.1 KB)
      • 008 Python Source Codes
      • Chapter2.Matplotlib.py (2.9 KB)
      • Chapter2.NumPy.py (1.1 KB)
      • Chapter2.Pandas.py (1.1 KB)
      • Chapter3.Preprocessing.py (2.6 KB)
      • Chapter5.Classification.py (3.3 KB)
      • Chapter6.Regression.py (4.3 KB)
      • Chapter7.Clustering.py (1.4 KB)
      • Chapter8.Parameter Tuning.py (1.9 KB)
      03 Data Preprocessing
      • 001 Reading and Modifying a Dataset.mp4 (154.6 MB)
      • 002 Statistics1.mp4 (34.0 MB)
      • 003 Statistics2.mp4 (207.5 MB)
      • 004 Statistics3 - Covariance.mp4 (107.5 MB)
      • 005 Missing Values1.mp4 (129.6 MB)
      • 006 Missing Values2.mp4 (219.4 MB)
      • 007 Outlier Detection1.mp4 (73.2 MB)
      • 008 Outlier Detection2.mp4 (130.7 MB)
      • 009 Outlier Detection3.mp4 (31.0 MB)
      • 010 Concatenation.mp4 (65.9 MB)
      • 011 Dummy Variable.mp4 (57.6 MB)
      • 012 Normalization.mp4 (186.9 MB)
      • 024 Data_Set.txt (0.6 KB)
      • 033 Data_New.txt (0.2 KB)
      • Readme.txt (0.1 KB)
      04 Machine Learning Introduction
      • 001 Learning Types.mp4 (45.4 MB)
      05 Supervised Learning - Classification
      • 001 Supervised Learning Models - Introduction and Understanding the Data.mp4 (233.8 MB)
      • 002 k-NN Concepts.mp4 (48.0 MB)
      • 003 k-NN Model Development.mp4 (140.7 MB)
      • 004 k-NN Training-Set and Test-Set Creation.mp4 (228.4 MB)
      • 005 Decision Tree Concepts.mp4 (25.6 MB)
      • 006 Decision Tree Model Development.mp4 (66.8 MB)
      • 007 Decision Tree - Cross Validation.mp4 (54.6 MB)
      • 008 Naive Bayes Concepts.mp4 (59.2 MB)
      • 009 Naive Bayes Model Development.mp4 (58.9 MB)
      • 010 Logistic Regression Concepts.mp4 (10.9 MB)
      • 011 Logistic Regression Model Development.mp4 (112.1 MB)
      • 012 Model Evaluation Concepts.mp4 (83.5 MB)
      • 013 Model Evaluation - Calculating with Python.mp4 (174.0 MB)
      07 Unsupervised Learning - Clustering Techniques
      • 001 Introduction.mp4 (38.1 MB)
      • 002 K-means Concepts1.mp4 (44.5 MB)
      • 003 K-means Concepts2.mp4 (21.3 MB)
      • 004 K-means Model Development1.mp4 (36.0 MB)
      • 005 K-means Model Development2.mp4 (103.8 MB)
      • 006 K-means - Model Evaluation.mp4 (102.4 MB)
      • 007 DBSCAN Concepts.mp4 (26.8 MB)
      • 008 DBSCAN Model Development.mp4 (86.9 MB)
      • 009 Hierarchical Clustering Concepts.mp4 (24.3 MB)
      • 010 Hierarchical Clustering Model Development.mp4 (145.9 MB)
      08 Hyper Parameter Optimization (Model Tuning)
      • 001 Introduction.mp4 (17.0 MB)
      • 002 Support Vector Regression - Model Tuning.mp4 (125.6 MB)
      • 003 K-Means - Model Tuning.mp4 (15.3 MB)
      • 004 k-NN - Model Tuning.mp4 (133.6 MB)
      • 005 Overfitting and Underfitting.mp4 (72.1 MB)

Description

Knowledge should not be limited to those who can afford it or those willing to pay for it.
If you found this course useful and are financially stable please consider supporting the creators by buying the course :)



Machine Learning & Data Science A-Z: Hands-on Python 2021
Learn NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Scipy and develop Machine Learning Models in Python



This course includes:
* 14.5 hours on-demand video




What you'll learn
* Understanding the basic concepts
* Complete tutorial about basic packages like Numpy and Pandas
* Data Visualization
* Data Preprocessing
* Understanding the concept behind the algorithms
* Developing different kinds of Machine Learning models
* Knowing how to optimize your models' hyperparameters
* Learn how to develop models based on the requirement of your future business


Are you interested in data science and machine learning, but you don't have any background, and you find the concepts confusing?

Are you interested in programming in Python, but you always afraid of coding?

I think this course is for you!

Even if you are familiar with machine learning, this course can help you to review all the techniques and understand the concept behind each term.

This course is completely categorized, and we don't start from the middle! We actually start from the concept of every term, and then we try to implement it in Python step by step. The structure of the course is as follows:

Chapter1: Introduction and all required installations

Chapter2: Useful Machine Learning libraries (NumPy, Pandas & Matplotlib)

Chapter3: Preprocessing

Chapter4: Machine Learning Types

Chapter5: Supervised Learning: Classification

Chapter6: Supervised Learning: Regression

Chapter7: Unsupervised Learning: Clustering

Chapter8: Model Tuning

Furthermore, you learn how to work with different real datasets and use them for developing your models. All the Python code templates that we write during the course together are available, and you can download them with the resource button of each section.

Remember! That this course is created for you with any background as all the concepts will be explained from the basic! Also, the programming in Python will be explained from the basic coding, and you just need to know the syntax of Python.



Download torrent
6.8 GB
seeders:7
leechers:8
Udemy - Machine Learning & Data Science A-Z Hands-on Python 2021

Trackers

tracker name
UDP://TRACKER.LEECHERS-PARADISE.ORG:6969/ANNOUNCE
UDP://TRACKER.COPPERSURFER.TK:6969/ANNOUNCE
udp://tracker.opentrackr.org:1337/announce
udp://tracker.openbittorrent.com:6969/announce
UDP://TRACKER.ZER0DAY.TO:1337/ANNOUNCE
UDP://EDDIE4.NL:6969/ANNOUNCE
udp://tracker.moeking.me:6969/announce
udp://retracker.lanta-net.ru:2710/announce
udp://open.stealth.si:80/announce
udp://www.torrent.eu.org:451/announce
udp://wassermann.online:6969/announce
udp://vibe.community:6969/announce
udp://valakas.rollo.dnsabr.com:2710/announce
udp://tracker0.ufibox.com:6969/announce
µTorrent compatible trackers list

Download torrent
6.8 GB
seeders:7
leechers:8
Udemy - Machine Learning & Data Science A-Z Hands-on Python 2021

Torrent hash: 79BAE004DB64A7190D279636F9EA14B12F1B4DD8