Machine Learning Tutorial for Beginners | Applied Machine Learning Algorithms
Welcome to my Channel...!
In this video we are going to see the basics of Applied Machine Learning . These are the fundamentals of Applied Machine Learning and essential trainings. we will see more and more in upcoming videos.
For any queries drop a mail at contact.missgoo@gmail.com
Share your thoughts about this video in the comment section and if you have any doubts post it in comment section.
BluePrism Playlist:- https://youtube.com/playlist?list=PLWMB5IYAuU6fwXy7lFh627J9buCMnPgRU
Thank You...!
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Where There is a Will There is a Way 💘
//Chapters and time splits
00:00:00-00:02:12 The power of algorithms in machine learning
00:02:13-00:03:35 What you should know
00:03:36-00:04:19 What tools you need
00:04:20-00:06:54 Defining model vs aldorithm
00:06:55-00:10:26 Process overview
00:10:27-00:17:58 Clean continous variables
00:17:59-00:24:46 Clean categorical variables
00:24:47-00:29:04 Split into train, validation and test set
00:29:05-00:32:15 What is logistic regression?
00:32:16-00:35:44 When should you consider using logistic regression?
00:35:45-00:41:21 What are the key hyperparameters to consider?
00:41:22-00:50:41 Fit a basic logistic regression model
00:50:42-00:55:28 What is Support Vector Machine?
00:55:29-00:58:23 When should you consider using SVM?
00:58:23-01:02:58 What are the key hyperparameters to consider?
01:02:59-01:09:43 Fit a basic SVM model
01:09:44-01:13:16 What is a multi-layer perceptron?
01:13:17-01:16:24 When should you consider using a multi-layer perceptron?
01:16:25-01:21:55 What are the key hyperparameters to consider?
01:21:56-01:30:00 Fit a basic multi-layer perceptron model
01:30:01-01:34:13 What is Random Forest?
01:34:14-01:36:20 When should you consider using Random Forest?
01:36:21-01:39:01 What are the key hyperparameters to consider?
01:39:02-01:43:26 Fit a basic Random Forest model
01:43:27-01:48:49 What is boosting?
01:48:50-01:51:35 When should you consider using boosting?
01:51:36-01:55:28 What are the key hyperparameters to consider?
01:55:29-02:00:51 Fit a basic boosting model
02:00:52-02:05:00 Why do you need to consider so many different model?
02:05:01-02:09:06 Conceptual comparison of algorithms
02:09:07-02:20:40 Final model selection and evaluation
02:20:41-02:22:18 Next steps
Видео Machine Learning Tutorial for Beginners | Applied Machine Learning Algorithms канала miss google
In this video we are going to see the basics of Applied Machine Learning . These are the fundamentals of Applied Machine Learning and essential trainings. we will see more and more in upcoming videos.
For any queries drop a mail at contact.missgoo@gmail.com
Share your thoughts about this video in the comment section and if you have any doubts post it in comment section.
BluePrism Playlist:- https://youtube.com/playlist?list=PLWMB5IYAuU6fwXy7lFh627J9buCMnPgRU
Thank You...!
→→→→→Visit Our Channel For More Videos←←←←←
🏹LIKE
🏹SHARE
🏹SUBSCRIBE
Where There is a Will There is a Way 💘
//Chapters and time splits
00:00:00-00:02:12 The power of algorithms in machine learning
00:02:13-00:03:35 What you should know
00:03:36-00:04:19 What tools you need
00:04:20-00:06:54 Defining model vs aldorithm
00:06:55-00:10:26 Process overview
00:10:27-00:17:58 Clean continous variables
00:17:59-00:24:46 Clean categorical variables
00:24:47-00:29:04 Split into train, validation and test set
00:29:05-00:32:15 What is logistic regression?
00:32:16-00:35:44 When should you consider using logistic regression?
00:35:45-00:41:21 What are the key hyperparameters to consider?
00:41:22-00:50:41 Fit a basic logistic regression model
00:50:42-00:55:28 What is Support Vector Machine?
00:55:29-00:58:23 When should you consider using SVM?
00:58:23-01:02:58 What are the key hyperparameters to consider?
01:02:59-01:09:43 Fit a basic SVM model
01:09:44-01:13:16 What is a multi-layer perceptron?
01:13:17-01:16:24 When should you consider using a multi-layer perceptron?
01:16:25-01:21:55 What are the key hyperparameters to consider?
01:21:56-01:30:00 Fit a basic multi-layer perceptron model
01:30:01-01:34:13 What is Random Forest?
01:34:14-01:36:20 When should you consider using Random Forest?
01:36:21-01:39:01 What are the key hyperparameters to consider?
01:39:02-01:43:26 Fit a basic Random Forest model
01:43:27-01:48:49 What is boosting?
01:48:50-01:51:35 When should you consider using boosting?
01:51:36-01:55:28 What are the key hyperparameters to consider?
01:55:29-02:00:51 Fit a basic boosting model
02:00:52-02:05:00 Why do you need to consider so many different model?
02:05:01-02:09:06 Conceptual comparison of algorithms
02:09:07-02:20:40 Final model selection and evaluation
02:20:41-02:22:18 Next steps
Видео Machine Learning Tutorial for Beginners | Applied Machine Learning Algorithms канала miss google
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24 октября 2022 г. 11:43:04
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