Udemy - Machine Learning & Data Science A-Z: Hands-on Python 2021 [FCS]

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01 Introduction
  • 001 Course Content.mp4 (17.1 MB)
  • 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)
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)
  • 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)
  • 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)
  • 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)
    06 Supervised Learning - Regression
    • 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)
    • 008 Random Forest Model Development.mp4 (246.2 MB)
    • 009 Support Vector Regression Concepts.mp4 (27.0 MB)
    • 010 Support Vector Regression Model Development.mp4 (121.0 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

Udemy - Machine Learning & Data Science A-Z: Hands-on Python 2021 [FCS]

Learn NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Scipy and develop Machine Learning Models in Python

Created by Navid Shirzadi
Last updated 3/2021
English
English [Auto]


For more Udemy Courses: https://freecoursesite.com
Our Forum for Discussion: https://forum.freecoursesite.com



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6.8 GB
seeders:12
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Udemy - Machine Learning & Data Science A-Z: Hands-on Python 2021 [FCS]


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