Udemy - Master Machine Learning with Practical Case Studies

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[ DevCourseWeb.com ] Udemy - Master Machine Learning with Practical Case Studies
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Linear Regression using House Rent Prediction
    • 1 - 1 - Introduction to Linear Regression.mp4.mp4 (59.1 MB)
    • 10 - 10 - Tabulate Results.mov.mp4 (12.5 MB)
    • 2 - 2 - Linear Regression.mov.mp4 (27.1 MB)
    • 3 - 3 - Import Data.mov.mp4 (34.4 MB)
    • 4 - 4 -Outlier Function.mov.mp4 (23.0 MB)
    • 5 - 5 - Visualization.mov.mp4 (33.9 MB)
    • 6 - 6 - Encode Data.mov.mp4 (30.4 MB)
    • 7 - 7 - Linear Model.mov.mp4 (26.9 MB)
    • 8 - 8 - Cross Validation and RMSE.mov.mp4 (10.1 MB)
    • 9 - 9 - Plot Predictions.mov.mp4 (28.9 MB)
    • House_Rent_Dataset.csv.csv (553.7 KB)
    10 - Naive Bayes using Fraud Classification
    • 1 - 1 - Introduction.mov.mp4 (39.1 MB)
    • 2 - 2 - Load Dataset.mov.mp4 (30.0 MB)
    • 2 - 3 - EDA.mov.mp4 (47.1 MB)
    • 3 - 4 - Modeling.mov.mp4 (26.8 MB)
    • 4 - 5 - Results.mov.mp4 (6.1 MB)
    11 - AdaBoost using Housing Price Prediction
    • 1 - 1 - Load Dataset.mov.mp4 (74.7 MB)
    • 2 - 2 - EDA.mov.mp4 (45.0 MB)
    • 3 - 3 - Modeling and Prediction.mov.mp4 (76.7 MB)
    • house_pricing_data.csv.csv (2.4 MB)
    12 - ARIMA
    • 1 - 1 - Introduction Part 1.mov.mp4 (16.6 MB)
    • 2 - 2 - Introduction Part 2.mov.mp4 (54.3 MB)
    • 3 - 3 - Stationarity Checks.mov.mp4 (16.4 MB)
    • 4 - 4 - Seasonal Decomposition Part 1.mov.mp4 (20.6 MB)
    • 5 - 5 - Seasonal Decomposition Part 2.mov.mp4 (43.1 MB)
    • 6 - 6 - Modeling and Prediction.mov.mp4 (41.8 MB)
    13 - XGB using Stock Dataset
    • 1 - 1 - Introduction.mov.mp4 (46.3 MB)
    • 2 - 2 - EDA.mov.mp4 (29.7 MB)
    • 3 - 3 - Data Preparation.mov.mp4 (42.8 MB)
    • 4 - 4 - Modeling.mov.mp4 (84.7 MB)
    • 5 - 5 - Backtesting.mov.mp4 (38.0 MB)
    • 6 - 6 - Results.mov.mp4 (29.8 MB)
    2 - Multicollinearity using Car Dataset
    • 1 - 1 - Introduction.mp4.mp4 (57.5 MB)
    • 2 - 2 - Linear Regression Data.mov.mp4 (28.4 MB)
    • 3 - 3 - EDA Part 1.mov.mp4 (44.1 MB)
    • 4 - 4 - EDA Part 2.mov.mp4 (50.6 MB)
    • 5 - 5 - EDA Part 3.mov.mp4 (14.0 MB)
    • 6 - 6 - Variance Inflation Factor.mov.mp4 (28.2 MB)
    • 7 - 7 - Predictions.mov.mp4 (78.6 MB)
    • Car_data.csv.csv (219.8 KB)
    3 - Logistic Regression using Credit Card Fraud Classification
    • 1 - 1 - Introduction.mov.mp4 (126.6 MB)
    • 2 - 2 - Logistic Regression.mp4.mp4 (41.2 MB)
    • 3 - 3 - EDA Part 1.mov.mp4 (35.7 MB)
    • 3 - 4 - EDA Part 2.mov.mp4 (33.1 MB)
    • 4 - 5 - Data Scaling Part 1.mov.mp4 (16.3 MB)
    • 5 - 6 - Data Scaling Part 2.mov.mp4 (45.8 MB)
    • 6 - 7 - Outlier Visualization.mov.mp4 (129.2 MB)
    • 7 - 8 - Predictions.mov.mp4 (42.5 MB)
    • 8 - 9 - Finding Best Parameters.mov.mp4 (33.4 MB)
    4 - SVC using Bank Customer Retirement Classification
    • 1 - 1 - SVC.mov.mp4 (44.5 MB)
    • Bank_Customer_retirement.csv.csv (14.9 KB)
    5 - Regularization using Sales Price Dataset
    • 1 - 1 - Regularization Part 1.mov.mp4 (89.5 MB)
    • 2 - 2 - Regularization Part 2.mov.mp4 (67.4 MB)
    • test.csv.csv (440.8 KB)
    • train.csv.csv (449.9 KB)
    6 - Decision Tree
    • 1 - 1 - EDA.mov.mp4 (63.8 MB)
    • 2 - 2 - Model Predictions.mov.mp4 (97.4 MB)
    7 - Random Forest using Housing Price Dataset
    • 1 - 1 - Introduction to Random Forest.mp4.mp4 (106.8 MB)
    • 10 - 10 - Comparison.mov.mp4 (13.7 MB)
    • 2 - 2 - Overview.mov.mp4 (11.9 MB)
    • 3 - 3 - Import Dataset.mov.mp4 (39.6 MB)
    • 4 - 4 - Metrics.mov.mp4 (9.2 MB)
    • 5 - 5 - Working of Random Forest.mov.mp4 (24.8 MB)
    • 6 - 6 - Linear Regression Modeling.mov.mp4 (52.3 MB)
    • 7 - 7 - Linear Regression Predictions.mov.mp4 (26.0 MB)
    • 8 - 8 - Random Forest Modeling.mov.mp4 (26.3 MB)
    • 9 - 9 - Random Forest Predictions.mov.mp4 (19.2 MB)
    • USA_Housing.csv.csv (709.2 KB)
    8 - PCA using Housing Price Prediction
    • 1 - 1 - Part 1.mov.mp4 (98.7 MB)
    • 2 - 2 - Part 2.mov.mp4 (85.7 MB)
    • test.csv.csv (440.8 KB)
    • train.csv.csv (449.9 KB)
    9 - K-Means using Social Media and Customer Segmentation
    • 1 - 1 - KMeans using Social Media Dataset Part 1.mov.mp4 (90.3 MB)
    • 2 - 2 - KMeans using Social Media Dataset Part 2.mov.mp4 (39.4 MB)
    • 3 - 3 - KMeans using Mall Customer Segmentation Part 1.mov.mp4 (65.7 MB)
    • 4 - 4 - KMeans using Mall Customer Segmentation Part 2.mov.mp4 (22.5 MB)
    • Live.csv.csv (553.8 KB)
    • Mall_Customers.csv.csv (3.9 KB)
    • Bonus Resources.txt (0.4 KB)

Description

Master Machine Learning with Practical Case Studies

https://DevCourseWeb.com

Published 8/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 6h 36m | Size: 2.93 GB

Hands on Machine Learning with Algorithms and Case Studies using different Datasets

What you'll learn
How to use Machine Learning Model for making Predictions for Real Life Problems
Understand Machine Learning to Apply in Real Practical Scenarios
Master Machine Learning techniques
Develop Insights for Data Wrangling, Data Cleansing, Data Enrichment, Data Analytics using Machine Learning
Build Linear Regression Model
Build Logistic Regression Model
Build Decision Tree Model
Understand ARIMA
Implement KMeans Clustering
Implement Naive Bayes
Understand Boosting Algorithms
Build XGBRegressor Model

Requirements
No prior Programming Experience required.



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Udemy - Master Machine Learning with Practical Case Studies

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2.9 GB
seeders:7
leechers:23
Udemy - Master Machine Learning with Practical Case Studies

Torrent hash: 0B718BCF61401937D8C3CC8ED462B89386040C93