Udemy - Machine Learning & Deep Learning in Python & R

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[FreeCourseLab.com] Udemy - Machine Learning & Deep Learning in Python & R 01 Introduction
  • 001 Introduction.mp4 (29.4 MB)
  • 002 Course Resources.html (1.2 KB)
02 Setting up Python and Jupyter Notebook
  • 001 Installing Python and Anaconda.mp4 (16.3 MB)
  • 002 This is a milestone!.mp4 (20.7 MB)
  • 003 Opening Jupyter Notebook.mp4 (65.2 MB)
  • 004 Introduction to Jupyter.mp4 (40.9 MB)
  • 005 Arithmetic operators in Python_ Python Basics.mp4 (12.7 MB)
  • 006 Strings in Python_ Python Basics.mp4 (64.4 MB)
  • 007 Lists, Tuples and Directories_ Python Basics.mp4 (60.3 MB)
  • 008 Working with Numpy Library of Python.mp4 (43.9 MB)
  • 009 Working with Pandas Library of Python.mp4 (46.9 MB)
  • 010 Working with Seaborn Library of Python.mp4 (40.4 MB)
03 Setting up R Studio and R crash course
  • 001 Installing R and R studio.mp4 (35.7 MB)
  • 002 Basics of R and R studio.mp4 (38.8 MB)
  • 003 Packages in R.mp4 (82.9 MB)
  • 004 Inputting data part 1_ Inbuilt datasets of R.mp4 (40.7 MB)
  • 005 Inputting data part 2_ Manual data entry.mp4 (25.5 MB)
  • 006 Inputting data part 3_ Importing from CSV or Text files.mp4 (60.1 MB)
  • 007 Creating Barplots in R.mp4 (96.7 MB)
  • 008 Creating Histograms in R.mp4 (42.0 MB)
04 Basics of Statistics
  • 001 Types of Data.mp4 (21.8 MB)
  • 002 Types of Statistics.mp4 (10.9 MB)
  • 003 Describing data Graphically.mp4 (65.4 MB)
  • 004 Measures of Centers.mp4 (38.6 MB)
  • 005 Measures of Dispersion.mp4 (22.8 MB)
05 Introduction to Machine Learning
  • 001 Introduction to Machine Learning.mp4 (109.2 MB)
  • 002 Building a Machine Learning Model.mp4 (39.5 MB)
06 Data Preprocessing
  • 001 Gathering Business Knowledge.mp4 (22.3 MB)
  • 002 Data Exploration.mp4 (20.5 MB)
  • 003 The Dataset and the Data Dictionary.mp4 (69.3 MB)
  • 004 Importing Data in Python.mp4 (27.8 MB)
  • 005 Importing the dataset into R.mp4 (13.1 MB)
  • 006 Univariate analysis and EDD.mp4 (24.2 MB)
  • 007 EDD in Python.mp4 (61.8 MB)
  • 008 EDD in R.mp4 (97.0 MB)
  • 009 Outlier Treatment.mp4 (24.5 MB)
  • 010 Outlier Treatment in Python.mp4 (70.3 MB)
  • 011 Outlier Treatment in R.mp4 (30.7 MB)
  • 012 Missing Value Imputation.mp4 (25.0 MB)
  • 013 Missing Value Imputation in Python.mp4 (23.4 MB)
  • 014 Missing Value imputation in R.mp4 (26.0 MB)
  • 015 Seasonality in Data.mp4 (17.0 MB)
  • 016 Bi-variate analysis and Variable transformation.mp4 (100.4 MB)
  • 017 Variable transformation and deletion in Python.mp4 (44.1 MB)
  • 018 Variable transformation in R.mp4 (55.4 MB)
  • 019 Non-usable variables.mp4 (20.2 MB)
  • 020 Dummy variable creation_ Handling qualitative data.mp4 (36.8 MB)
  • 021 Dummy variable creation in Python.mp4 (26.5 MB)
  • 022 Dummy variable creation in R.mp4 (44.0 MB)
  • 023 Correlation Analysis.mp4 (71.6 MB)
  • 024 Correlation Analysis in Python.mp4 (55.3 MB)
  • 025 Correlation Matrix in R.mp4 (83.1 MB)
07 Linear Regression
  • 001 The Problem Statement.mp4 (9.4 MB)
  • 002 Basic Equations and Ordinary Least Squares (OLS) method.mp4 (43.4 MB)
  • 003 Assessing accuracy of predicted coefficients.mp4 (92.1 MB)
  • 004 Assessing Model Accuracy_ RSE and R squared.mp4 (43.6 MB)
  • 005 Simple Linear Regression in Python.mp4 (63.4 MB)
  • 006 Simple Linear Regression in R.mp4 (40.8 MB)
  • 007 Multiple Linear Regression.mp4 (34.3 MB)
  • 008 The F - statistic.mp4 (56.0 MB)
  • 009 Interpreting results of Categorical variables.mp4 (22.5 MB)
  • 010 Multiple Linear Regression in Python.mp4 (69.7 MB)
  • 011 Multiple Linear Regression in R.mp4 (62.4 MB)
  • 012 Test-train split.mp4 (41.9 MB)
  • 013 Bias Variance trade-off.mp4 (25.1 MB)
  • 014 Test train split in Python.mp4 (44.9 MB)
  • 015 Test-Train Split in R.mp4 (75.6 MB)
  • 016 Regression models other than OLS.mp4 (16.5 MB)
  • 017 Subset selection techniques.mp4 (79.1 MB)
  • 018 Subset selection in R.mp4 (63.5 MB)
  • 019 Shrinkage methods_ Ridge and Lasso.mp4 (33.3 MB)
  • 020 Ridge regression and Lasso in Python.mp4 (128.8 MB)
  • 021 Ridge regression and Lasso in R.mp4 (103.4 MB)
  • 022 Heteroscedasticity.mp4 (14.5 MB)
08 Classification Models_ Data Preparation
  • 001 The Data and the Data Dictionary.mp4 (79.0 MB)
  • 002 Data Import in Python.mp4 (22.1 MB)
  • 003 Importing the dataset into R.mp4 (13.5 MB)
  • 004 EDD in Python.mp4 (77.6 MB)
  • 005 EDD in R.mp4 (66.5 MB)
  • 006 Outlier treatment in Python.mp4 (47.3 MB)
  • 007 Outlier Treatment in R.mp4 (25.4 MB)
  • 008 Missing Value Imputation in Python.mp4 (22.6 MB)
  • 009 Missing Value imputation in R.mp4 (19.0 MB)
  • 010 Variable transformation and Deletion in Python.mp4 (29.3 MB)
  • 011 Variable transformation in R.mp4 (38.0 MB)
  • 012 Dummy variable creation in Python.mp4 (26.4 MB)
  • 013 Dummy variable creation in R.mp4 (44.4 MB)
09 The Three classification models
  • 001 Three Classifiers and the problem statement.mp4 (20.3 MB)
  • 002 Why can't we use Linear Regression_.mp4 (16.9 MB)
10 Logistic Regression
  • 001 Logistic Regression.mp4 (32.9 MB)
  • 002 Training a Simple Logistic Model in Python.mp4 (47.9 MB)
  • 003 Training a Simple Logistic model in R.mp4 (25.6 MB)
  • 004 Result of Simple Logistic Regression.mp4 (26.9 MB)
  • 005 Logistic with multiple predictors.mp4 (8.5 MB)
  • 006 Training multiple predictor Logistic model in Python.mp4 (26.3 MB)
  • 007 Training multiple predictor Logistic model in R.mp4 (15.8 MB)
  • 008 Confusion Matrix.mp4 (21.1 MB)
  • 009 Creating Confusion Matrix in Python.mp4 (51.3 MB)
  • 010 Evaluating performance of model.mp4 (35.2 MB)
  • 011 Evaluating mod

Description

Udemy - Machine Learning & Deep Learning in Python & R

Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R

Created by Start-Tech Academy
Last updated 4/2021

For more Udemy Courses: https://freecourselab.com



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Udemy - Machine Learning & Deep Learning in Python & R

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Download torrent
13.1 GB
seeders:10
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Udemy - Machine Learning & Deep Learning in Python & R

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