[ FreeCourseWeb ] Udemy - Predictive Modeling with Python

seeders: 6
leechers: 10
updated:
Download torrent
Added by freecoursewb in Other > Tutorials
Download Fast Safe Anonymous
movies, software, shows...
  • Downloads: 108
  • Language: English

Files

[ FreeCourseWeb.com ] Udemy - Predictive Modeling with Python
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 01 Introduction and Installation
    • 001 Introduction to Predictive Modelling with Python.en.srt (8.8 KB)
    • 001 Introduction to Predictive Modelling with Python.mp4 (46.0 MB)
    • 002 Installation.en.srt (10.5 KB)
    • 002 Installation.mp4 (43.0 MB)
    02 Data Preprocessing
    • 001 Data Preprocessing.en.srt (16.3 KB)
    • 001 Data Preprocessing.mp4 (75.4 MB)
    • 002 Dataframe.en.srt (9.7 KB)
    • 002 Dataframe.mp4 (43.4 MB)
    • 003 Imputer.en.srt (12.0 KB)
    • 003 Imputer.mp4 (71.2 MB)
    • 004 Create Dumies.en.srt (7.2 KB)
    • 004 Create Dumies.mp4 (39.2 MB)
    • 005 Splitting Dataset.en.srt (10.7 KB)
    • 005 Splitting Dataset.mp4 (58.8 MB)
    • 006 Features Scaling.en.srt (7.9 KB)
    • 006 Features Scaling.mp4 (31.9 MB)
    03 Linear Regression
    • 001 Introduction to Linear Regression.en.srt (13.1 KB)
    • 001 Introduction to Linear Regression.mp4 (36.6 MB)
    • 002 Estimated Regression Model.en.srt (10.9 KB)
    • 002 Estimated Regression Model.mp4 (35.7 MB)
    • 003 Import the Library.en.srt (8.4 KB)
    • 003 Import the Library.mp4 (40.8 MB)
    • 004 Plot.en.srt (9.2 KB)
    • 004 Plot.mp4 (42.4 MB)
    • 005 Tip Example.en.srt (10.9 KB)
    • 005 Tip Example.mp4 (50.6 MB)
    • 006 Print Function.en.srt (7.1 KB)
    • 006 Print Function.mp4 (43.9 MB)
    04 Salary Prediction
    • 001 Introduction to Salary Dataset.en.srt (9.3 KB)
    • 001 Introduction to Salary Dataset.mp4 (35.8 MB)
    • 002 Fitting Linear Regression.en.srt (8.3 KB)
    • 002 Fitting Linear Regression.mp4 (50.9 MB)
    • 003 Fitting Linear Regression Continue.en.srt (8.7 KB)
    • 003 Fitting Linear Regression Continue.mp4 (26.7 MB)
    • 004 Prediction from the Model.en.srt (8.3 KB)
    • 004 Prediction from the Model.mp4 (40.4 MB)
    • 005 Prediction from the Model Continue.mp4 (48.9 MB)
    05 Profit Prediction
    • 001 Introduction to Multiple Linear Regression.en.srt (7.9 KB)
    • 001 Introduction to Multiple Linear Regression.mp4 (30.9 MB)
    • 002 Creating Dummies.en.srt (14.0 KB)
    • 002 Creating Dummies.mp4 (78.6 MB)
    • 003 Removing one Dummy and Splitting Dataset.en.srt (8.0 KB)
    • 003 Removing one Dummy and Splitting Dataset.mp4 (44.3 MB)
    • 004 Training Set and Predictions.en.srt (7.8 KB)
    • 004 Training Set and Predictions.mp4 (53.7 MB)
    • 005 Stats Models to Make Optimal Model.en.srt (11.3 KB)
    • 005 Stats Models to Make Optimal Model.mp4 (62.0 MB)
    • 006 Steps to Make Optimal Model.en.srt (9.7 KB)
    • 006 Steps to Make Optimal Model.mp4 (53.4 MB)
    • 007 Making Optimal Model by Backward Elimination.en.srt (10.9 KB)
    • 007 Making Optimal Model by Backward Elimination.mp4 (74.0 MB)
    • 008 Adjusted R Square.en.srt (10.8 KB)
    • 008 Adjusted R Square.mp4 (65.9 MB)
    • 009 Final Optimal Model Implementation.en.srt (13.0 KB)
    • 009 Final Optimal Model Implementation.mp4 (78.6 MB)
    06 Boston Housing
    • 001 Introduction to Jupyter Notebook.en.srt (14.1 KB)
    • 001 Introduction to Jupyter Notebook.mp4 (55.0 MB)
    • 002 Understanding Dataset and Problem Statement.en.srt (11.3 KB)
    • 002 Understanding Dataset and Problem Statement.mp4 (49.6 MB)
    • 003 Working with Correlation Plots.en.srt (8.6 KB)
    • 003 Working with Correlation Plots.mp4 (44.0 MB)
    • 004 Working with Correlation Plots Continue.en.srt (7.9 KB)
    • 004 Working with Correlation Plots Continue.mp4 (42.8 MB)
    • 005 Correlation Plot and Splitting Dataset.en.srt (16.5 KB)
    • 005 Correlation Plot and Splitting Dataset.mp4 (76.1 MB)
    • 006 MLR Model with Sklearn and Predictions.en.srt (6.6 KB)
    • 006 MLR Model with Sklearn and Predictions.mp4 (34.4 MB)
    • 007 MLR model with Statsmodels and Predictions.en.srt (9.9 KB)
    • 007 MLR model with Statsmodels and Predictions.mp4 (53.8 MB)
    • 008 Getting Optimal model with Backward Elimination Approach.en.srt (9.2 KB)
    • 008 Getting Optimal model with Backward Elimination Approach.mp4 (73.0 MB)
    • 009 RMSE Calculation and Multicollinearity Theory.en.srt (10.3 KB)
    • 009 RMSE Calculation and Multicollinearity Theory.mp4 (47.5 MB)
    • 010 VIF Calculation.en.srt (7.5 KB)
    • 010 VIF Calculation.mp4 (39.7 MB)
    • 011 VIF and Correlation Plots.en.srt (12.0 KB)
    • 011 VIF and Correlation Plots.mp4 (68.9 MB)
    • 011 VIF and Correlation Plots.mp4.vtx (462.0 KB)
    07 Logistic Regression
    • 001 Introduction to Logistic Regression.en.srt (12.8 KB)
    • 001 Introduction to Logistic Regression.mp4 (32.6 MB)
    • 002 Understanding Problem Statement and Splitting.mp4 (59.2 MB)
    • 003 Scaling and Fitting Logistic Regression Model.en.srt (6.0 KB)
    • 003 Scaling and Fitting Logistic Regression Model.mp4 (41.7 MB)
    • 004 Prediction and Introduction to Confusion Matrix.en.srt (13.4 KB)
    • 004 Prediction and Introduction to Confusion Matrix.mp4 (63.9 MB)
    • 005 Confusion Matrix Explanation.en.srt (8.1 KB)
    • 005 Confusion Matrix Explanation.mp4 (32.6 MB)
    • 006 Checking Model Performance using Confusion Matrix.en.srt (15.0 KB)
    • 006 Checking Model Performance using Confusion Matrix.mp4 (88.0 MB)
    • 007 Plots Understanding.en.srt (9.1 KB)
    • 007 Plots Understanding.mp4 (57.7 MB)
    • 008 Plots Understanding Continue.en.srt (9.9 KB)
    • 008 Plots Understanding Continue.mp4 (56.1 MB)
    08 Diabetes
    • 001 Introduction and data Preprocessing.en.srt (7.0 KB)
    • 001 Introduction and data Preprocessing.mp4 (46.6 MB)
    • 002 Fitting Model with Sklearn Library.en.srt (6.6 KB)
    • 002 Fitting Model with Sklearn Library.mp4 (42.1 MB)
    • 003 Fitting Model with Statmodel Library.en.srt (11.6 KB)
    • 003 Fitting Model with Statmodel Library.mp4 (78.3 MB)

Description

Predictive Modeling with Python

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.54 GB | Duration: 9h 25m
Think with a predictive mindset and understand well the basics of the techniques used in prediction with this course
What you'll learn
Learn the predictive modeling in python, linear regression, logistic regression, the fitting model with a sci-kit learn library, the fitting model with stat model library, ROC curves, backward elimination approach, stats model package, etc.
You will be guided through the installation of the required software. Data Pre-processing, which includes Data frame, splitting dataset, feature scaling, etc. You will gain an edge

Description
Predictive Modeling is the use of data and statistics to predict the outcome of the data models. This prediction finds its utility in almost all areas from sports, to TV ratings, corporate earnings, and technological advances. Predictive modeling is also called predictive analytics. With the help of predictive analytics, we can connect data to effective action about the current conditions and future events. Also, we can enable the business to exploit patterns and which are found in historical data to identify potential risks and opportunities before they occur. Python is used for predictive modeling because Python-based frameworks give us results faster and also help in the planning of the next steps based on the results.

Our course ensures that you will be able to think with a predictive mindset and understand well the basics of the techniques used in prediction. Critical thinking is very important to validate models and interpret the results. Hence, our course material emphasizes on hardwiring this similar kind of thinking ability. You will have good knowledge about the predictive modeling in python, linear regression, logistic regression, the fitting model with a sci-kit learn library, the fitting model with stat model library, ROC curves, backward elimination approach, stats model package, etc.

In this course, you will get an introduction to Predictive Modelling with Python. You will be guided through the installation of the required software. Data Pre-processing, which includes Data frame, splitting dataset, feature scaling, etc. You will gain an edge on Linear Regression, Salary Prediction, Logistic Regression. You will get to work on various datasets dealing with Credit Risk and Diabetes.

Download More Courses Visit and Support Us -->> https://FreeCourseWeb.com



Download torrent
3.5 GB
seeders:6
leechers:10
[ FreeCourseWeb ] Udemy - Predictive Modeling with Python

Trackers

tracker name
udp://tracker.torrent.eu.org:451/announce
udp://tracker.tiny-vps.com:6969/announce
http://tracker.foreverpirates.co:80/announce
udp://tracker.cyberia.is:6969/announce
udp://exodus.desync.com:6969/announce
udp://explodie.org:6969/announce
udp://tracker.opentrackr.org:1337/announce
udp://9.rarbg.to:2780/announce
udp://tracker.internetwarriors.net:1337/announce
udp://ipv4.tracker.harry.lu:80/announce
udp://open.stealth.si:80/announce
udp://9.rarbg.to:2900/announce
udp://9.rarbg.me:2720/announce
udp://opentor.org:2710/announce
µTorrent compatible trackers list

Download torrent
3.5 GB
seeders:6
leechers:10
[ FreeCourseWeb ] Udemy - Predictive Modeling with Python

Torrent hash: 6F39AA3216C3887311CECB3DF02A1420E1121A67