| 1 - 1 - EDA.mov.mp4 | 63.8 MB | ||
| 1 - 1 - Introduction Part 1.mov.mp4 | 16.6 MB | ||
| 1 - 1 - Introduction to Linear Regression.mp4.mp4 | 59.1 MB | ||
| 1 - 1 - Introduction to Random Forest.mp4.mp4 | 106.8 MB | ||
| 1 - 1 - Introduction.mov.mp4 | 126.6 MB | ||
| 1 - 1 - Introduction.mp4.mp4 | 57.5 MB | ||
| 1 - 1 - KMeans using Social Media Dataset Part 1.mov.mp4 | 90.3 MB | ||
| 1 - 1 - Load Dataset.mov.mp4 | 74.7 MB | ||
| 1 - 1 - Part 1.mov.mp4 | 98.7 MB | ||
| 1 - 1 - Regularization Part 1.mov.mp4 | 89.5 MB | ||
| 1 - 1 - SVC.mov.mp4 | 44.5 MB | ||
| 10 - 10 - Comparison.mov.mp4 | 13.7 MB | ||
| 10 - 10 - Tabulate Results.mov.mp4 | 12.5 MB | ||
| 2 - 2 - EDA.mov.mp4 | 29.7 MB | ||
| 2 - 2 - Introduction Part 2.mov.mp4 | 54.3 MB | ||
| 2 - 2 - KMeans using Social Media Dataset Part 2.mov.mp4 | 39.4 MB | ||
| 2 - 2 - Linear Regression Data.mov.mp4 | 28.4 MB | ||
| 2 - 2 - Linear Regression.mov.mp4 | 27.1 MB | ||
| 2 - 2 - Load Dataset.mov.mp4 | 30 MB | ||
| 2 - 2 - Logistic Regression.mp4.mp4 | 41.2 MB | ||
| 2 - 2 - Model Predictions.mov.mp4 | 97.4 MB | ||
| 2 - 2 - Overview.mov.mp4 | 11.9 MB | ||
| 2 - 2 - Part 2.mov.mp4 | 85.7 MB | ||
| 2 - 2 - Regularization Part 2.mov.mp4 | 67.4 MB | ||
| 2 - 3 - EDA.mov.mp4 | 47.1 MB | ||
| 3 - 3 - Data Preparation.mov.mp4 | 42.8 MB | ||
| 3 - 3 - EDA Part 1.mov.mp4 | 35.7 MB | ||
| 3 - 3 - Import Data.mov.mp4 | 34.4 MB | ||
| 3 - 3 - Import Dataset.mov.mp4 | 39.6 MB | ||
| 3 - 3 - KMeans using Mall Customer Segmentation Part 1.mov.mp4 | 65.7 MB | ||
| 3 - 3 - Modeling and Prediction.mov.mp4 | 76.7 MB | ||
| 3 - 3 - Stationarity Checks.mov.mp4 | 16.4 MB | ||
| 3 - 4 - EDA Part 2.mov.mp4 | 33.1 MB | ||
| 3 - 4 - Modeling.mov.mp4 | 26.8 MB | ||
| 4 - 4 - EDA Part 2.mov.mp4 | 50.6 MB | ||
| 4 - 4 - KMeans using Mall Customer Segmentation Part 2.mov.mp4 | 22.5 MB | ||
| 4 - 4 - Metrics.mov.mp4 | 9.2 MB | ||
| 4 - 4 - Modeling.mov.mp4 | 84.7 MB | ||
| 4 - 4 - Seasonal Decomposition Part 1.mov.mp4 | 20.6 MB | ||
| 4 - 4 -Outlier Function.mov.mp4 | 23 MB | ||
| 4 - 5 - Data Scaling Part 1.mov.mp4 | 16.3 MB | ||
| 4 - 5 - Results.mov.mp4 | 6.1 MB | ||
| 5 - 5 - Backtesting.mov.mp4 | 38 MB | ||
| 5 - 5 - EDA Part 3.mov.mp4 | 14 MB | ||
| 5 - 5 - Seasonal Decomposition Part 2.mov.mp4 | 43.1 MB | ||
| 5 - 5 - Visualization.mov.mp4 | 33.9 MB | ||
| 5 - 5 - Working of Random Forest.mov.mp4 | 24.8 MB | ||
| 5 - 6 - Data Scaling Part 2.mov.mp4 | 45.8 MB | ||
| 6 - 6 - Encode Data.mov.mp4 | 30.4 MB | ||
| 6 - 6 - Linear Regression Modeling.mov.mp4 | 52.3 MB | ||
| 6 - 6 - Modeling and Prediction.mov.mp4 | 41.8 MB | ||
| 6 - 6 - Results.mov.mp4 | 29.8 MB | ||
| 6 - 6 - Variance Inflation Factor.mov.mp4 | 28.2 MB | ||
| 6 - 7 - Outlier Visualization.mov.mp4 | 129.2 MB | ||
| 7 - 7 - Linear Model.mov.mp4 | 26.9 MB | ||
| 7 - 7 - Linear Regression Predictions.mov.mp4 | 26 MB | ||
| 7 - 7 - Predictions.mov.mp4 | 78.6 MB | ||
| 7 - 8 - Predictions.mov.mp4 | 42.5 MB | ||
| 8 - 8 - Cross Validation and RMSE.mov.mp4 | 10.1 MB | ||
| 8 - 8 - Random Forest Modeling.mov.mp4 | 26.3 MB | ||
| 8 - 9 - Finding Best Parameters.mov.mp4 | 33.4 MB | ||
| 9 - 9 - Plot Predictions.mov.mp4 | 28.9 MB | ||
| 9 - 9 - Random Forest Predictions.mov.mp4 | 19.2 MB | ||
| Bank_Customer_retirement.csv.csv | 14.9 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Car_data.csv.csv | 219.8 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| House_Rent_Dataset.csv.csv | 553.7 KB | ||
| Live.csv.csv | 553.8 KB | ||
| Mall_Customers.csv.csv | 3.9 KB | ||
| USA_Housing.csv.csv | 709.2 KB | ||
| house_pricing_data.csv.csv | 2.4 MB | ||
| test.csv.csv | 440.8 KB | ||
| train.csv.csv | 449.9 KB | ||
| ▲ 80 total files | |||
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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